Relevant bibliographies by topics / EfficientDet

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Academic literature on the topic 'EfficientDet'

Author: Grafiati
Published: 13 April 2023

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Journal articles on the topic "EfficientDet"

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Liu, Chao, and Shouying Lin. "Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning." Journal of Physics: Conference Series 2218, no. 1 (March 1, 2022): 012039. http://dx.doi.org/10.1088/1742-6596/2218/1/012039.

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Abstract In this research paper, the Chinese beehive culture in Fujian Province, China is used as the carrier, and the Mini-EfficientDet deep neural network after migration is used to identify common species at the hive door, that is, the identification of Chinese bees, wasps and cockroaches in the form of nymphs. In this paper, we define the modified model as Mini-EfficientDet by compressing the initial EfficientDet model and adding the category imbalance function, which makes it focus more on the recognition and classification of small targets while ensuring the recognition accuracy. Through the test on the MSCOCO2017 data set, it is concluded that when the backbone network adopts EfficientNet B7, it shows strong detection accuracy in detecting targets of various scales, which confirms the role of the category imbalance function proposed in this paper and the efficiency of the improved EfficientDet model. Detection accuracy. The pre-trained model is transferred to the field of beehive species detection through migration learning, that is, after the post-training of the self-collected data set, the detection accuracy of Chinese bee, cockroach, and wasp is 98.66%, 83.71%, and 82.06%. It has made sufficient algorithmic preparations for the later detection and early warning system of beehive species invasion.
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Nawaz, Marriam, Tahira Nazir, Ali Javed, Usman Tariq, Hwan-Seung Yong, Muhammad Attique Khan, and Jaehyuk Cha. "An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization." Sensors 22, no. 2 (January 7, 2022): 434. http://dx.doi.org/10.3390/s22020434.

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Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.
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Ahmed, Syed Sohail, Zahid Mehmood, Imran Ahmad Awan, and Rehan Mehmood Yousaf. "A Novel Technique for Handwritten Digit Recognition Using Deep Learning." Journal of Sensors 2023 (January 30, 2023): 1–15. http://dx.doi.org/10.1155/2023/2753941.

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Handwritten digit recognition (HDR) shows a significant application in the area of information processing. However, correct recognition of such characters from images is a complicated task due to immense variations in the writing style of people. Moreover, the occurrence of several image artifacts like the existence of intensity variations, blurring, and noise complicates this process. In the proposed method, we have tried to overcome the aforementioned limitations by introducing a deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. Initially, the input images are annotated to exactly show the region of interest (ROI). In the next phase, these images are used to train the EfficientNet-B4-based EfficientDet-D4 model to detect and categorize the numerals into their respective classes from zero to nine. We have tested the proposed model over the MNIST dataset to demonstrate its efficacy and attained an average accuracy value of 99.83%. Furthermore, we have accomplished the cross-dataset evaluation on the USPS database and achieved an accuracy value of 99.10%. Both the visual and reported experimental results show that our method can accurately classify the HDR from images even with the varying writing style and under the presence of various sample artifacts like noise, blurring, chrominance, position, and size variations of numerals. Moreover, the introduced approach is capable of generalizing well to unseen cases which confirms that the EfficientDet-D4 model is an effective solution to numeral recognition.
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Saleh, Mubarak Auwalu, Zubaida Said Ameen, Chadi Altrjman, and Fadi Al-Turjman. "Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet." Sustainability 14, no. 18 (September 12, 2022): 11413. http://dx.doi.org/10.3390/su141811413.

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Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology is employed to make life easier and link nearly everything. Many researchers have created object detectors; however, there is a demand for lightweight versions that can fit into smartphones and other edge devices. The goal of this research is to demonstrate the notion of employing a mobile application that can detect statues efficiently on mobile applications, and also improve the performance of the models by employing the Gaussian Smoothing Filter (GSF). In this study, three object detection models, EfficientDet—D0, EfficientDet—D2 and EfficientDet—D4, were trained on original and smoothened images; moreover, their performance was compared to find a model efficient detection score that is easy to run on a mobile phone. EfficientDet—D4, trained on smoothened images, achieves a Mean Average Precision (mAP) of 0.811, an mAP-50 of 1 and an mAP-75 of 0.90.
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Wang, Yanfeng, Tao Wang, Xin Zhou, Weiwei Cai, Runmin Liu, Meigen Huang, Tian Jing, et al. "TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2262549.

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In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.
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Liao, Jianhao, Jiayu Zou, Ao Shen, Jinfu Liu, and Xiaofei Du. "Cigarette end detection based on EfficientDet." Journal of Physics: Conference Series 1748 (January 2021): 062015. http://dx.doi.org/10.1088/1742-6596/1748/6/062015.

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Jia, Jiaqi, Min Fu, Xuefeng Liu, and Bing Zheng. "Underwater Object Detection Based on Improved EfficientDet." Remote Sensing 14, no. 18 (September 8, 2022): 4487. http://dx.doi.org/10.3390/rs14184487.

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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.
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Nawaz, Marriam, Tahira Nazir, Jamel Baili, Muhammad Attique Khan, Ye Jin Kim, and Jae-Hyuk Cha. "CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model." Diagnostics 13, no. 2 (January 9, 2023): 248. http://dx.doi.org/10.3390/diagnostics13020248.

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The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduced in health departments. However, the reliable detection and classification of particular illnesses in chest X-ray samples is still a complicated issue because of the complex structure of radiographs, e.g., the large exposure dynamic range. Moreover, the incidence of various image artifacts and extensive inter- and intra-category resemblances further increases the difficulty of chest disease recognition procedures. The aim of this study was to resolve these existing problems. We propose a deep learning (DL) approach to the detection of chest abnormalities with the X-ray modality using the EfficientDet (CXray-EffDet) model. More clearly, we employed the EfficientNet-B0-based EfficientDet-D0 model to compute a reliable set of sample features and accomplish the detection and classification task by categorizing eight categories of chest abnormalities using X-ray images. The effective feature computation power of the CXray-EffDet model enhances the power of chest abnormality recognition due to its high recall rate, and it presents a lightweight and computationally robust approach. A large test of the model employing a standard database from the National Institutes of Health (NIH) was conducted to demonstrate the chest disease localization and categorization performance of the CXray-EffDet model. We attained an AUC score of 0.9080, along with an IOU of 0.834, which clearly determines the competency of the introduced model.
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Zheng, Xin, Feng Chen, Liming Lou, Pengle Cheng, and Ying Huang. "Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network." Remote Sensing 14, no. 3 (January 23, 2022): 536. http://dx.doi.org/10.3390/rs14030536.

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To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.
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Carmo, Diedre, Israel Campiotti, Lívia Rodrigues, Irene Fantini, Gustavo Pinheiro, Daniel Moraes, Rodrigo Nogueira, Leticia Rittner, and Roberto Lotufo. "Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals." Health Informatics Journal 27, no. 3 (July 2021): 146045822110330. http://dx.doi.org/10.1177/14604582211033017.

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The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.
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Dissertations / Theses on the topic "EfficientDet"

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Michieli, Francesco <1991&gt. "Stimatori campionari non distorti della frontiera efficiente." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5196.

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La tesi presenta la teoria finanziaria classica di portafoglio e discute della distorsione statistica degli stimatori della frontiera efficiente, verificando (tramite l'analisi di serie storiche con il software statistico "R") nuovi approcci proposti in letteratura.
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Cattai, Francesco <1993&gt. "Analisi sugli stimatori corretti della frontiera efficiente." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12562.

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Currà, Pier Nicola. "Alma.Domus: residenza eco-efficiente per Solar Decathlon Europe." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3619/.

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Bovet, Gérôme. "Architecture évolutive et efficiente du Web des bâtiments." Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0033/document.

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Les bâtiments sont de plus en plus équipés avec des réseaux d’automatisation dédiés, visant à réduire la consommation d’énergie ainsi que d’optimiser le confort. D’un autre côté, nous observons l’arrivée de capteurs et actionneurs liés à l’Internet des objets, pouvant naturellement se connecter à des réseaux IP. Dû à des contraintes d’obsolescence ou imposées par les propriétés physiques des bâtiments, il n’est pas rare que différentes technologies doivent cohabiter. Celles-ci fonctionnant avec des modèles et protocoles différents rend le développement de systèmes d’automatisation globaux compliqué. Les modèles classiques de systèmes distribués ne sont pas adaptés aux problématiques des réseaux de capteurs. Le paradigme du Web des objets est basé sur des ressources et vise quand à lui d’uniformiser la couche applicative entre différents objets à l’aide des technologies du Web, essentiellement HTTP et REST. Dans cette thèse, nous nous basons sur le Web des objets afin de créer un framework dédié au bâtiments intelligents, permettant aux développeurs de concevoir des applications composites sans connaissances des différentes technologies sous-jacentes. Grâce aux technologies Web, nous pouvons offrir des services homogènes tout en profitant des ressources disponibles à l’intérieur du réseau (capteurs et actionneurs), formant un nuage auto-géré. Dans le but de doter les bâtiments d’une plus grande intelligence, l’apprentissage automatique, souvent réservé aux experts, est rendu accessible à travers des interfaces Web cachant la complexité des processus
Buildings are increasingly equipped with dedicated automation networks, aiming to reduce the energy consumption and to optimize the comfort. On the other hand, we see the arrival of sensors and actuators related to the Internet of Things, which can naturally connect to IP networks. Due to constraints imposed by the obsolescence or physical properties of buildings, it is not uncommon that different technologies have to coexist. These networks operate with different models and protocols, making the development of global automation systems difficult. Traditional models of distributed systems are not adapted to the context of sensor networks. The paradigm of the Web of Things is resource-based and strives to standardize the application layer of different objects using Web technologies, primarily HTTP and REST. In this thesis, we use the Web of Things to create a framework dedicated to smart buildings, allowing developers to develop composite applications without knowledge of the underlying technologies. By relying on Web technologies, we can provide seamless service while reusing the available resources within the network (sensors and actuators), forming a self-managed cloud. In order to equip the buildings with a higher-level intelligence, machine learning, often reserved for experts, is made accessible through Web interfaces hiding the complexity of the process
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Bonelli, Michael. "Gestione Efficiente di Eventi Complessi su Piattaforma IoT ThingWorx." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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Nella prima parte di questa tesi viene introdotto il concetto di Internet of Things. Vengono discussi gli elementi costituitivi fondamentali di tale tecnologia, le differenti architetture proposte nel corso degli anni e le sfide che devono ancora essere affrontate per vedere realizzato l’IoT. Questa prima parte si conclude inoltre con due esempi di applicazione dell’IoT. Questi due esempi, Smart City e Smart Healthcare, hanno l’obbiettivo di evidenziare quali sono i vantaggi ed i servizi che possono essere offerti all’utente finale una volta applicato l’IoT. Nel secondo capitolo invece, vengono presentate le funzionalità della piattaforma IoT ThingWorx, la quale mette a disposizione un ambiente di sviluppo per applicazioni IoT con l’obbiettivo di ridurre i tempi e quindi anche i costi di sviluppo delle stesse. Questa piattaforma cerca di ridurre al minimo la necessità di scrivere codice, utilizzando un sistema di sviluppo di tipo “Drag and Drop”. ThingWorx mette anche a disposizione degli SDK per facilitare la programmazione dei device, gestendo soprattutto la parte di comunicazione nodo – piattaforma. Questo argomento viene trattato ampiamente nella parte finale di questo capitolo dopo aver visto quali sono i concetti fondamentali di modellazione e rappresentazione dei dati sui quali si basa la piattaforma. Nel terzo e ultimo capitolo di questa tesi viene presentato innanzitutto il tutorial Android di ThingWorx. Svolgere e successivamente estendere il tutorial ha evidenziato alcune limitazioni del modello iniziale e questo ci ha portato a progettare e sviluppare il componente Aggregated & Complex Event Manager per la gestione di eventi complessi e che permette di sgravare parzialmente la piattaforma da tale compito. La tesi si conclude evidenziando, tramite dei test, alcune differenze fra la situazione iniziale nella quale il componente non viene utilizzato e la situazione finale, nella quale invece viene usato.
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REITANO, RAFFAELLA. "Oro Blu : gestione efficiente della risorsa acqua nell'ambito Mediterraneo." Doctoral thesis, Università IUAV di Venezia, 2013. http://hdl.handle.net/11578/278310.

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Le, Magoarou Luc. "Matrices efficientes pour le traitement du signal et l'apprentissage automatique." Thesis, Rennes, INSA, 2016. http://www.theses.fr/2016ISAR0008/document.

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Les matrices, en tant que représentations des applications linéaires en dimension finie, jouent un rôle central en traitement du signal et des images et en apprentissage automatique. L'application d'une matrice de rang plein à un vecteur implique a priori un nombre d'opérations arithmétiques de l'ordre du nombre d'entrées non-nulles que contient la matrice. Cependant, il existe des matrices pouvant être appliquées bien plus rapidement, cette propriété étant d'ailleurs un des fondements du succès de certaines transformations linéaires, telles que la transformée de Fourier ou la transformée en ondelettes. Quelle est cette propriété? Est-elle vérifiable aisément? Peut-on approcher des matrices quelconques par des matrices ayant cette propriété? Peut-on estimer des matrices ayant cette propriété? La thèse s'attaque à ces questions en explorant des applications telles que l'apprentissage de dictionnaire à implémentation efficace, l'accélération des itérations d'algorithmes de résolution de de problèmes inverses pour la localisation de sources, ou l'analyse de Fourier rapide sur graphe
Matrices, as natural representation of linear mappings in finite dimension, play a crucial role in signal processing and machine learning. Multiplying a vector by a full rank matrix a priori costs of the order of the number of non-zero entries in the matrix, in terms of arithmetic operations. However, matrices exist that can be applied much faster, this property being crucial to the success of certain linear transformations, such as the Fourier transform or the wavelet transform. What is the property that allows these matrices to be applied rapidly ? Is it easy to verify ? Can weapproximate matrices with ones having this property ? Can we estimate matrices having this property ? This thesis investigates these questions, exploring applications such as learning dictionaries with efficient implementations, accelerating the resolution of inverse problems or Fast Fourier Transform on graphs
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Grigoli, Francesco. "Studio dei codici, trasmissione e correzione efficiente di un messaggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20965/.

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L'elaborato si prefigge di descrivere come avviene la codifica, la decodifica e la correzione di errori in una trasmissione dati, sfruttando l'entropia di Shannon, la codifica di Huffmann e i codici di Hamming.
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D'ORAZIO, ALESSIO. "Tecniche e materiali innovativi per una produzione efficiente ed ecosostenibile." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/253167.

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La competitività delle aziende dipende dalla capacità di innovare prodotti e processi, tenendo in conto la gestione sostenibile delle risorse e la riduzione di impatti ambientali e sociali per il miglioramento della qualità della vita. Ciò implica un profondo cambiamento verso sistemi di produzione e di consumo più sostenibili accompagnato da un cambiamento culturale e di stili di vita, ovvero una transizione verso una green economy. L’eco-innovazione è il principale strumento della green economy e, dunque, dello sviluppo sostenibile. In particolare, grazie all’introduzione di una o più tecnologie ecoinnovative, le aziende possono conseguire una riduzione degli impatti ambientali osservabile lungo tutto il ciclo di vita dei propri prodotti e/o processi. L’ecoinnovazione riguarda lo sviluppo di tecnologie di produzione più sostenibili ma anche l’utilizzo di materiali leggeri ed ecoinnovativi, soprattutto nel settore dell’automotive e in quello aeronautico, dove è più significativa la riduzione delle emissioni di CO2 connesse ad un risparmio del consumo di carburante. Alla luce di tutto ciò, lo scopo del presente lavoro di tesi è quello di investigare da più prospettive il concetto di sostenibilità ambientale nei processi manifatturieri.
Nowadays, companies’ competitiveness depends on their ability to innovate products and processes, keeping in mind that a better quality of life is given by the sustainable management of resources and a lower impact on society and the environment. This involves a deep change towards more sustainable production and consumption systems along with a change in culture and life style, i.e. a transition towards a green economy. Eco-innovation is the main instrument of green economy and, consequently, of sustainable development. In particular, thanks to the introduction of an environmentally friendly system, companies can achieve a reduction of their carbon footprint that can be observed along the entire life cycle of their products or processes. The eco-innovation can consist in the development of manufacturing technologies more sustainable as well as in the development of new Eco-friendly and lightweight materials, especially in the automotive and aeronautical sectors, where the environmental benefit is greater with the reduction of carbon emission, coupled with the savings in fuel. In light of this, the overall purpose of the work developed in this thesis is to analyze the environmental sustainability concept in the manufacturing processes through a Multiple-Perspective Approach.
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Vetrano, Vittorio <1977&gt. "Biomasse e loro quantificazione economica per un efficiente uso dell'energia." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/2187/1/Vetrano_Vittorio_tesi.pdf.pdf.

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Biomasses and their possible use as energy resource are of great interest today, and the general problem of energy resources as well. In the present study the key questions of the convenience, from both energy and economy standpoints, have been addressed without any bias: the problem has been handled starting from “philosophical” bases disregarding any pre-settled ideology or political trend, but simply using mathematical approaches as logical tools for defining balances in a right way. In this context quantitative indexes such as LCA and EROEI have been widely used, together with multicriteria methods (such as ELECTRE) as decision supporting tools. This approach permits to remove mythologies, such as the unrealistic concept of clean energy, or the strange idea of biomasses as a magic to solve every thing in the field of the energy. As a consequence the present study aims to find any relevant aspect potentially useful for the society, looking at any possible source of energy without prejudices but without unrealistic expectations too. For what concerns biomasses, we studied in great details four very different cases of study, in order to have a scenario as various as much we can. A relevant result is the need to use biomasses together with other more efficient sources, especially recovering by-products from silviculture activities: but attention should be paid to the transportation and environmental costs. Another relevant result is the very difficult possibility of reliable evaluation of dedicated cultures as sources for “biomasses for energy”: the problem has to be carefully evaluated case-by-case, because what seems useful in a context, becomes totally disruptive in another one. In any case the concept itself of convenience is not well defined at a level of macrosystem: it seems more appropriate to limit this very concept at a level of microsystem, considering that what sounds fine in a limited well defined microsystem may cause great damage in another slightly different, or even very similar, microsystem. This approach seems the right way to solve the controversy about the concept of convenience.
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Books on the topic "EfficientDet"

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Hol, A. M. Gewogen recht: Billijkheid en efficientie bij onrechtmatige daad. Deventer: Kluwer, 1993.

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La société efficiente: Pourquoi fait-il si bon vivre au Canada? [Montréal, Que.]: Presses de l'Université de Montréal, 2002.

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Orfeo, Maria, ed. La riforma dell'amministrazione e il sistema universitario tra semplificazione e trasparenza. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-138-6.

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Nel processo di riforma che ormai da anni caratterizza le Pubbliche Amministrazioni, la semplificazione e la trasparenza rappresentano due aspetti innovativi ed importanti per il modello organizzativo delle Università. Una tematica complessa che coinvolge molteplici profili e sollecita la riflessione su questioni ancora aperte, di significativo spessore. I relatori nei loro interventi ne esplorano i diversi aspetti evidenziando le ragioni per proseguire verso un cambiamento complessivo dell'amministrazione finalizzato a rendere la sua azione più efficiente, rapida ed economica.
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Granatstein, J. L. For efficient and effective military forces =: Des forces militaires efficientes et efficaces. Ottawa, Ont: Dept. of National Defence = Ministère de la défense nationale, 1997.

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Madagascar. Une bonne gouvernance n'est efficiente sans une intégrité certaine: Le code d'éthique. Antananarivo]: Repoblikan'i Madagasikara, Autorité de régulation des marchés publics, 2008.

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La solidarietà efficiente: Storia e prospettive del credito cooperativo in Italia : 1883-2000. Roma [etc.]: Laterza, 2002.

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Drouin, Francine. Évaluer pour enseigner: À la découverte d'une pédagogie efficiente auprès de l'élève sourd. Toronto: Ministère de l'éducation et de la formation de l'Ontario, 1993.

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Ciappei, Cristiano, and Massimiliano Pellegrini, eds. Facility management for global care. Florence: Firenze University Press, 2010. http://dx.doi.org/10.36253/978-88-6453-088-8.

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The aim of this work is to bring the study of facility management, that is the management of the services connected with the maintenance and valorisation of real estate, to a higher and more complete level. We have sought to overcome the – albeit inevitable – engineering/efficientist approach, to arrive at an all-round promotion and analysis of the discipline, hinging on the concept of service. This means, first and foremost, rediscovering the relational aspect apropos the clientele and, starting from this, moving towards a restructuring of the service where the aim is to meet personal requirements rather than purely technical standards. The aspiration, underscored in the title, is in fact that of arriving at a "global care" of the person.
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Una gestione bancaria efficiente: La Cassa di risparmio di Udine dalle origini alla prima guerra mondiale. Udine: Forum, 2007.

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1957-, Frattini Franco, Canivet Guy, and Badinter Robert, eds. Pour une justice économique efficiente en Europe: Enjeux et perspectives d'une harmonisation : Colloque du 4 décembre 2007. Bruxelles: Bruylant, 2009.

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Book chapters on the topic "EfficientDet"

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Xu, Yunhua, Xuejun Wang, and Shuyuan Li. "Track Surface Defect Detection Based on EfficientDet." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 56–66. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9909-2_7.

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Deokar, Abhishek, and Mohamed El-Sharkawy. "EfficientNeXt: EfficientNet for Embedded Systems." In Proceedings of Seventh International Congress on Information and Communication Technology, 449–59. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2394-4_41.

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Koonce, Brett. "EfficientNet." In Convolutional Neural Networks with Swift for Tensorflow, 109–23. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2_10.

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Mulcahy, Eoghan, Pepijn Van de Ven, and John Nelson. "Aerial Object Detection for Water-Based Search & Rescue." In Communications in Computer and Information Science, 344–54. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_27.

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AbstractResponding to a water rescue situation is challenging. First responders need access to data as quickly as possible to increase the likelihood of a successful rescue. Using aerial imagery systems is especially useful in a search and rescue scenario because it provides a higher dimensional view of the search environment. Unmanned aerial vehicles can be easily used to acquire aerial image data. During water-based search and rescue scenarios, first responders sometimes deploy an inflatable marker called a rescue danbuoy. The danbuoy is fitted with a small conical sack known as a drogue, this ensures that the marker is not blown off course by the wind and instead follows the flow of the body of water. Tracking the danbuoy as it moves is of utmost importance in a water rescue. We present a new data-set “VisBuoy” with imagery containing instances of danbuoy markers and boats in real-world water-based settings. We also show how using various deep learning-based computer vision techniques, we can autonomously detect danbuoy instances in aerial imagery. We compare the performance of four state-of-the-art object detectors Faster RCNN Retinanet, Efficientdet and YOLOv5 on the “VisBuoy” data-set, to find the best detector for this task. We then propose a best model with a precision score of 74% which can be used in search and rescue operations to detect inflatable danbuoy markers in water-based settings.
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Upadhyay, Prashant, Bhavya Garg, Anant Tyagi, and Arin Tyagi. "Face Recognition Using EfficientNet." In Proceedings of International Conference on Recent Trends in Computing, 679–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8825-7_58.

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Trinh, Quoc-Huy, Trong-Hieu Nguyen Mau, Radmir Zosimov, and Minh-Van Nguyen. "EfficientNet for Brain-Lesion Classification." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 249–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08999-2_20.

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Hoang, Van-Thanh, Van-Dung Hoang, and Kang-Hyun Jo. "Rethinking Mobile Inverted Bottleneck Convolution for EfficientNet." In Computational Intelligence Methods for Green Technology and Sustainable Development, 435–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19694-2_39.

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Padmashree, G., Shruti G. Wagle, and A. K. Karunakar. "Disguise Face Classification Using EfficientNet Deep Learning." In Human-Centric Smart Computing, 305–14. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5403-0_26.

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Kadri, Rahma, Mohamed Tmar, and Bassem Bouaziz. "Alzheimer’s Disease Prediction Using EfficientNet and Fastai." In Knowledge Science, Engineering and Management, 452–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82147-0_37.

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Aruleba, Idowu, and Serestina Viriri. "Deep Learning for Age Estimation Using EfficientNet." In Advances in Computational Intelligence, 407–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85030-2_34.

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Conference papers on the topic "EfficientDet"

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Ubaid, Muhammad Talha, Amara Kiran, Muhammad Tayyab Raja, Umme Aliza Asim, Abdou Darboe, and Muhammad Asad Arshed. "Automatic Helmet Detection using EfficientDet." In 2021 International Conference on Innovative Computing (ICIC). IEEE, 2021. http://dx.doi.org/10.1109/icic53490.2021.9693093.

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Tan, Mingxing, Ruoming Pang, and Quoc V. Le. "EfficientDet: Scalable and Efficient Object Detection." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01079.

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Singh, Ritik, Shubham Shetty, Gaurav Patil, and Pramod J. Bide. "Helmet Detection Using Detectron2 and EfficientDet." In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9579953.

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Yang, Zengquan, Han Huang, Fuming Xia, and Zhen Shi. "Smoke Detection Algorithm Based on Improved EfficientDet." In ICDLT 2022: 2022 6th International Conference on Deep Learning Technologies. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3556677.3556678.

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Fan, Xinlei, Fan Wang, Shuai Pang, Jiaxing Wang, and Wenjing Wang. "Safety helmet wearing detection based on EfficientDet algorithm." In 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), edited by Ligu Zhu. SPIE, 2022. http://dx.doi.org/10.1117/12.2641445.

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Jin, Jintao, Wei Feng, Qujiang Lei, Guangchao Gui, Xiuhao Li, Zhonghui Deng, and Weijun Wang. "Defect Detection of Printed Circuit Boards Using EfficientDet." In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). IEEE, 2021. http://dx.doi.org/10.1109/icsip52628.2021.9688801.

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Cao, Liangben, Xixin Zhang, Jingyu Pu, Siyuan Xu, Xinlin Cai, and Zhiyong Li. "The Field Wheat Count Based on the Efficientdet Algorithm." In 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2020. http://dx.doi.org/10.1109/iciscae51034.2020.9236918.

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Lin, Kaihao, Junyan Chen, Aoge Chen, and Hu Huang. "Application of the EfficientDet Algorithm in Traffic Flow Statistics." In 2020 16th International Conference on Computational Intelligence and Security (CIS). IEEE, 2020. http://dx.doi.org/10.1109/cis52066.2020.00038.

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Liu, Rui, and Peiyang Wei. "The ChuanbeiMu Herb Identification Based on the EfficientDet Algorithm." In 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI). IEEE, 2021. http://dx.doi.org/10.1109/cisai54367.2021.00085.

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Srikanth, Anirudh, Aparajith Srinivasan, Haresh Indrajit, and Venkateswaran N. "Contactless Object Identification Algorithm for the Visually Impaired using EfficientDet." In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2021. http://dx.doi.org/10.1109/wispnet51692.2021.9419427.

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Reports on the topic "EfficientDet"

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Corlin Christensen, Rasmus, Martin Hearson, and Tovony Randriamanalina. Une tablée plus grande, mais toujours le même menu ? Evaluer l’inclusion des pays en développement dans les négociations fiscales mondiales. Institute of Development Studies, December 2020. http://dx.doi.org/10.19088/ictd.2020.006.

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Depuis 2013, la structure officielle de l’organe de décision sur les politiques fiscales mondiales au niveau de l’OCDE a changé. Les décisions ne sont plus prises par les 37 membres de l’OCDE, mais par 137 pays représentant toutes les régions du monde et tous les niveaux de développement à travers le ‘cadre inclusif’. Les documents officiels insistent sur le fait que tous les pays participent sur un pied d’égalité, mais certains participants et observateurs ont souligné que les pays en développement se heurtent à des obstacles d’ordre pratique qui engendrent, dans les faits, une participation inégale. Dans cette publication, nous évaluons ces déclarations principalement à partir de 48 interviews menées avec des négociateurs, des décideurs politiques et autres acteurs impliqués dans les discussions mondiales sur la fiscalité. Nous observons que l’explosion du nombre des adhésions formelles des pays en développement n’a pas suscité un changement radical sur l’influence des ces derniers. Ce que les chiffres bruts laissaient présager. Nous observons que l’explosion du nombre d’adhésions formelles n’a pas suscité le changement radical dans l’influence des pays en développement que les chiffres bruts laissaient présager. Cela s’explique par une combinaison d’obstacles structurels non spécifiques au cadre inclusif et d’aspects problématiques dans le mode de fonctionnement de l’OCDE. À ce jour, les pays à faible revenu ont tout de même obtenu quelques modestes résultats et on observe les signes d’un acheminement progressif vers une présence plus efficiente. Nous développons une typologie de mécanismes ayant engendré des résultats positifs : l’association avec les efforts des États plus puissants, l’anticipation des besoins des pays à faible revenu par le secrétariat de l’OCDE et par d’autres entités, la collaboration pour former des coalitions plus puissantes, et l’émergence de négociateurs experts ayant une autorité individuelle.
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