Thematische Bibliographien / Object detection in images

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Auswahl der wissenschaftlichen Literatur zum Thema „Object detection in images“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Object detection in images"

Shin, Su-Jin, Seyeob Kim, Youngjung Kim, and Sungho Kim. "Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images." Remote Sensing 12, no. 17 (2020): 2734. http://dx.doi.org/10.3390/rs12172734.

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Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always

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Moitra, Sabyasachi, and Sambhunath Biswas. "Object Detection in Images: A Survey." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 10–29. http://dx.doi.org/10.21275/sr23330184650.

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Jung, Sejung, Won Hee Lee, and Youkyung Han. "Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles." Remote Sensing 13, no. 18 (2021): 3660. http://dx.doi.org/10.3390/rs13183660.

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Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building

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Wang, Zhiyuan, Shujun Men, Yuntian Bai, et al. "Improved Small Object Detection Algorithm CRL-YOLOv5." Sensors 24, no. 19 (2024): 6437. http://dx.doi.org/10.3390/s24196437.

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Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module i

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Vajda, Peter, Ivan Ivanov, Lutz Goldmann, Jong-Seok Lee, and Touradj Ebrahimi. "Robust Duplicate Detection of 2D and 3D Objects." International Journal of Multimedia Data Engineering and Management 1, no. 3 (2010): 19–40. http://dx.doi.org/10.4018/jmdem.2010070102.

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In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters

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Sejr, Jonas Herskind, Peter Schneider-Kamp, and Naeem Ayoub. "Surrogate Object Detection Explainer (SODEx) with YOLOv4 and LIME." Machine Learning and Knowledge Extraction 3, no. 3 (2021): 662–71. http://dx.doi.org/10.3390/make3030033.

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Due to impressive performance, deep neural networks for object detection in images have become a prevalent choice. Given the complexity of the neural network models used, users of these algorithms are typically given no hint as to how the objects were found. It remains, for example, unclear whether an object is detected based on what it looks like or based on the context in which it is located. We have developed an algorithm, Surrogate Object Detection Explainer (SODEx), that can explain any object detection algorithm using any classification explainer. We evaluate SODEx qualitatively and quan

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Wu, Jingqian, and Shibiao Xu. "From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images." Remote Sensing 13, no. 13 (2021): 2620. http://dx.doi.org/10.3390/rs13132620.

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Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the

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Vu, Thang C., Thanh V. Nguyen, Tao V. Nguyen, et al. "Object Detection in Remote Sensing Images Using Deep Learning: From Theory to Applications in Intelligent Transportation Systems." Journal of Future Artificial Intelligence and Technologies 2, no. 2 (2025): 227–41. https://doi.org/10.62411/faith.3048-3719-114.

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Object detection for sensing images is one of the promising research directions in computer vision. Applications for object detection from remote sensing images play an important role in analyzing aerial or satellite imagery. Benefits include applications in monitoring buildings and infrastructure, transportation, supporting search and rescue or responding to natural disasters, and environmental research. However, detecting objects in remote sensing images is difficult due to the diversity of shapes and sizes, viewing angles of objects, and complex background environments. In this paper, the a

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Fidelis, Nfwan Gonten. "Improved Fast YOLO One-Stage Object Detection Algorithm for Detecting Objects in Images." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 5 (2025): 1–8. https://doi.org/10.35940/ijies.D4597.12050525.

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<strong>Abstract: </strong>The use of a Convolutional neural network (CNN) has gained wide recommendation in the research community, especially in the area of vision systems (Object detection). The recent CNN recorded Various advancements in object detection in images with tremendous accuracy but still faced challenges of high time complexity. A one-stage object detection algorithm called YOLO (You Only Look Once), used for object classification and localization, performs great, especially in detecting objects in real time. In this study, we proposed an improved, fast YOLO CNN-based algorithm

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T. Sanjeeva Kumar. "Moving Object Detection in Aerial Images using DeepSORT with Faster R-CNN." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 391–404. https://doi.org/10.52783/jisem.v10i4s.531.

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Aerial imagery is increasingly utilized in various applications, including surveillance, disaster management, agriculture, and urban planning. Detecting and tracking moving objects within aerial images is a crucial task for these applications. This paper presents a novel approach to moving object detection in aerial images, combining the Faster R-CNN (Region-based Convolutional Neural Network) for object detection and the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm for object tracking. The proposed method leverages the strengths of both techniques, enabling accurate and effic

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Dissertationen zum Thema "Object detection in images"

Simonelli, Andrea. "3D Object Detection from Images." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/353602.

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Remarkable advancements in the field of Computer Vision, Artificial Intelligence and Machine Learning have led to unprecedented breakthroughs in what machines are able to achieve. In many tasks such as in Image Classification in fact, they are now capable of even surpassing human performance. While this is truly outstanding, there are still many tasks in which machines lag far behind. Walking in a room, driving on an highway, grabbing some food for example. These are all actions that feel natural to us but can be quite unfeasible for them. Such actions require to identify and localize objec

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Mohan, Anuj 1976. "Robust object detection in images by components." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80554.

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Kok, R. "An object detection approach for cluttered images." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53281.

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Thesis (MScEng)--Stellenbosch University, 2003.<br>ENGLISH ABSTRACT: We investigate object detection against cluttered backgrounds, based on the MINACE (Minimum Noise and Correlation Energy) filter. Application of the filter is followed by a suitable segmentation algorithm, and the standard techniques of global and local thresholding are compared to watershed-based segmentation. The aim of this approach is to provide a custom region-based object detection algorithm with a concise set of regions of interest. Two industrial case studies are examined: diamond detection in X-ray images, and

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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more

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Papageorgiou, Constantine P. "A Trainable System for Object Detection in Images and Video Sequences." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/5566.

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This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classifier to learn the differe

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Gonzalez-Garcia, Abel. "Image context for object detection, object context for part detection." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28842.

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Objects and parts are crucial elements for achieving automatic image understanding. The goal of the object detection task is to recognize and localize all the objects in an image. Similarly, semantic part detection attempts to recognize and localize the object parts. This thesis proposes four contributions. The first two make object detection more efficient by using active search strategies guided by image context. The last two involve parts. One of them explores the emergence of parts in neural networks trained for object detection, whereas the other improves on part detection by adding objec

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Gadsby, David. "Object recognition for threat detection from 2D X-ray images." Thesis, Manchester Metropolitan University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493851.

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This thesis examines methods to identify threat objects inside airport handheld passenger baggage. The work presents techniques for the enhancement and classification of objects from 2-dimensional x-ray images. It has been conducted with the collaboration of Manchester Aviation Services and uses test images from real x-ray baggage machines. The research attempts to overcome the key problem of object occlusion that impedes the performance of x-ray baggage operators identifying threat objects such as guns and knifes in x-ray images. Object occlusions can hide key information on the appearance of

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Vi, Margareta. "Object Detection Using Convolutional Neural Network Trained on Synthetic Images." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153224.

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Training data is the bottleneck for training Convolutional Neural Networks. A larger dataset gives better accuracy though also needs longer training time. It is shown by finetuning neural networks on synthetic rendered images, that the mean average precision increases. This method was applied to two different datasets with five distinctive objects in each. The first dataset consisted of random objects with different geometric shapes. The second dataset contained objects used to assemble IKEA furniture. The neural network with the best performance, trained on 5400 images, achieved a mean averag

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Rickert, Thomas D. (Thomas Dale) 1975. "Texture-based statistical models for object detection in natural images." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80570.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (p. 63-65).<br>by Thomas D. Rickert.<br>S.B.and M.Eng.

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Jangblad, Markus. "Object Detection in Infrared Images using Deep Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355221.

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In the master thesis about object detection(OD) using deep convolutional neural network(DCNN), the area of OD is being tested when being applied to infrared images(IR). In this thesis the, goal is to use both long wave infrared(LWIR) images and short wave infrared(SWIR) images taken from an airplane in order to train a DCNN to detect runways, Precision Approach Path Indicator(PAPI) lights, and approaching lights. The purpose for detecting these objects in IR images is because IR light transmits better than visible light under certain weather conditions, for example, fog. This system could then

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Bücher zum Thema "Object detection in images"

Bogusław Cyganek. Object Detection and Recognition in Digital Images. John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118618387.

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Lee, Chin-Hwa. Similarity counting architecture for object detection. Naval Postgraduate School, 1986.

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Wosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Konstanz, 1999.

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Yuan-Liang, Tang, Devadiga Sadashiva, United States. National Aeronautics and Space Administration., Pennsylvania State University. Dept. of Electrical and Computer Engineering., and Langley Research Center, eds. A model-based approach for detection of objects in low resolution passive millimeter wave images. Dept. of Electrical and Computer Engineering, The Pennsylvania State University, 1993.

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Poonkuntran, S., Balamurugan Balusamy, and Rajesh Kumar Dhanraj. Object Detection with Deep Learning Models. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736.

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Shaikh, Soharab Hossain, Khalid Saeed, and Nabendu Chaki. Moving Object Detection Using Background Subtraction. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07386-6.

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K, Jurgen Ronald, and Society of Automotive Engineers, eds. Object detection, collision warning & avoidance systems. SAE International, 2007.

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Shufelt, Jefferey. Geometric Constraints for Object Detection and Delineation. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5273-4.

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Jiang, Xiaoyue, Abdenour Hadid, Yanwei Pang, Eric Granger, and Xiaoyi Feng, eds. Deep Learning in Object Detection and Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.

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Goulermas, John. Hough transform techniques for circular object detection. UMIST, 1996.

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Buchteile zum Thema "Object detection in images"

Yavari, Abulfazl, and H. R. Pourreza. "Object Detection in Foveated Images." In Technological Developments in Networking, Education and Automation. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9151-2_49.

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Topkar, V., B. Kjell, and A. Sood. "Object detection in noisy images." In Active Perception and Robot Vision. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77225-2_34.

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Chung, Youngsun, Daeyoung Gil, and Ghang Lee. "Optimal Number of Cue Objects for Photo-Based Indoor Localization." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.98.

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Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects re

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Koeva, Svetla. "Multilingual Image Corpus." In European Language Grid. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_22.

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AbstractThe ELG pilot project Multilingual Image Corpus (MIC 21) provides a large image dataset with annotated objects and multilingual descriptions in 25 languages. Our main contributions are: the provision of a large collection of highquality, copyright-free images; the formulation of an ontology of visual objects based on WordNet noun hierarchies; precise manual correction of automatic image segmentation and annotation of object classes; and association of objects and images with extended multilingual descriptions. The dataset is designed for image classification, object detection and seman

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Kumar, Nitin, Maheep Singh, M. C. Govil, E. S. Pilli, and Ajay Jaiswal. "Salient Object Detection in Noisy Images." In Advances in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34111-8_15.

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Ziran, Zahra, and Simone Marinai. "Object Detection in Floor Plan Images." In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99978-4_30.

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Schneiderman, Henry. "Learning Statistical Structure for Object Detection." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_54.

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Zhang, Yu-Jin. "Saliency Object Detection." In A Selection of Image Analysis Techniques. CRC Press, 2022. http://dx.doi.org/10.1201/b23131-4.

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Aburasain, R. Y., E. A. Edirisinghe, and M. Y. Zamim. "A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks." In Digital Interaction and Machine Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_2.

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AbstractMulti-class object detection has a rapid evolution in the last few years with the rise of deep Convolutional Neural Networks (CNNs) learning based, in particular. However, the success approaches are based on high resolution ground level images and extremely large volume of data as in COCO and VOC datasets. On the other hand, the availability of the drones has been increased in the last few years and hence several new applications have been established. One of such is understanding drone footage by analysing, detecting, recognizing different objects in the covered area. In this study co

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Konferenzberichte zum Thema "Object detection in images"

Danko, Tomáš, Miloš Oravec, Veronika Kurilovrá, and Jarmila Pavlovicova. "Small Object Detection in Fundus Images." In 2024 International Symposium ELMAR. IEEE, 2024. http://dx.doi.org/10.1109/elmar62909.2024.10694511.

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Nagarajan, G., Abhiram Piratla, and Karthikeya Golla. "Object Detection in Images Using Machine Learning." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914893.

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Burges, Marvin, Sebastian Zambanini, and Robert Sablatnig. "Interactive Object Detection for Tiny Objects in Large Remotely Sensed Images." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00461.

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Venkatesh, K., and Mohammad Farukh Hashmi. "Object Detection for Remote Sensing Images Using YOLOv8." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725328.

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Park, Kyu Tae, Jaehyung Kim, Ryoonhan Kim, JungHan Kwon, and Min Cheol Lee. "Training YOLOv8 Object Detection Model with Synthetic Images." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773352.

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Chang, Qingling, Taijie Zhang, Wenhao Liu, and Yan Cui. "FEDet: Feature Enhancement Object Detection with Panoramic Images." In 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI). IEEE, 2024. http://dx.doi.org/10.1109/seai62072.2024.10674235.

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Ye, Wen, Xinyi Zhu, Yanjing Guo, and Mingyu Huang. "Infrared Object Detection Based YOLOv8s from UAV Images." In 2024 11th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2024. https://doi.org/10.1109/dsa63982.2024.00054.

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Chand, K. Vinay, K. Ashwini, M. Yogesh, and G. Prasanna Jyothi. "Object Detection in Remote Sensing Images Using YOLOv8." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859885.

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Lucas, Evan, Ali Awad, Anthony Geglio, et al. "Underwater Image Enhancement and Object Detection: Are Poor Object Detection Results On Enhanced Images Due to Missing Human Labels?" In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00167.

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He, Jiacan, Qi Yin, Jian Jiang, Biao Wu, and Zhixiong Ma. "Quality Evaluation of Synthetic Images for Camera Object Detection Model." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. SAE International, 2025. https://doi.org/10.4271/2025-01-7145.

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<div class="section abstract"><div class="htmlview paragraph">The realism of simulation scenarios significantly impacts the performance of object detection models. The confidence of synthetic images generated by current simulators remains controversial. Verifying the quality of synthetic image generation methods is a prerequisite and foundation for using these synthetic images for robustness testing and optimization of object detection models. Therefore, this paper aims to propose a quality quantification evaluation scheme for synthetic images based on the object detection model. T

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Berichte der Organisationen zum Thema "Object detection in images"

Repperger, Daniel W., Alan R. Pinkus, Julie A. Skipper, and Christina D. Schrider. Stochastic Resonance Investigation of Object Detection in Images. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada472478.

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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l

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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, th

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Heisele, Bernd, Thomas Serre, Sayan Mukherjee, and Tomaso Poggio. Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada458821.

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Jewell, D. A., H. P. White, and L. M. Campbell. Automated detection of mine tailings via object-based classification of Sentinel-2 images. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/pgw1ywkvvm.

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Clausen, Jay, Christopher Felt, Michael Musty, et al. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43442.

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperat

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Mazari, Mehran, Yahaira Nava-Gonzalez, Ly Jacky Nhiayi, and Mohamad Saleh. Smart Highway Construction Site Monitoring Using Artificial Intelligence. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2336.

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Construction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes.

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Christie, Benjamin, Osama Ennasr, and Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42385.

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Integrating thermal (or infrared) imagery on a robotics platform allows Unmanned Ground Vehicles (UGV) to function in low-visibility environments, such as pure darkness or low-density smoke. To maximize the effectiveness of this approach we discuss the modifications required to integrate our low-visibility object detection model on a Robot Operating System (ROS). Furthermore, we introduce a method for reporting detected objects while performing Simultaneous Localization and Mapping (SLAM) by generating bounding boxes and their respective transforms in visually challenging environments.

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Tao, Yang, Victor Alchanatis, and Yud-Ren Chen. X-ray and stereo imaging method for sensitive detection of bone fragments and hazardous materials in de-boned poultry fillets. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7695872.bard.

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As Americans become increasingly health conscious, they have increased their consumptionof boneless white and skinless poultry meat. To the poultry industry, accurate detection of bonefragments and other hazards in de-boned poultry meat is important to ensure food quality andsafety for consumers. X-ray imaging is widely used for internal material inspection. However,traditional x-ray technology has limited success with high false-detection errors mainly becauseof its inability to consistently recognize bone fragments in meat of uneven thickness. Today’srapid grow-out practices yield chicken bo

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