WAID: A Large-Scale Dataset for Wildlife Detection with Drones DOI Creative Commons
Chao Mou, Tengfei Liu, Chengcheng Zhu

и другие.

Applied Sciences, Год журнала: 2023, Номер 13(18), С. 10397 - 10397

Опубликована: Сен. 17, 2023

Drones are widely used for wildlife monitoring. Deep learning algorithms key to the success of monitoring with drones, although they face problem detecting small targets. To solve this problem, we have introduced SE-YOLO model, which incorporates a channel self-attention mechanism into advanced real-time object detection algorithm YOLOv7, enabling model perform effectively on However, there is another barrier; lack publicly available UAV aerial datasets hampers research algorithms. fill gap, present large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), contains 14,375 images different environmental conditions, covering six species and multiple habitat types. We conducted statistical analysis experiment, an comparison generalization experiment. The experiment demonstrated characteristics both quantitatively intuitively. experiments compared types as well method perspective practical application UAVs experimental results show that suitable study UAVs, most effective in scenario, mAP up 0.983. This brings new methods, data, inspiration field by UAVs.

Язык: Английский

A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS DOI Creative Commons
Juan Terven, Diana‐Margarita Córdova‐Esparza, Julio-Alejandro Romero-González

и другие.

Machine Learning and Knowledge Extraction, Год журнала: 2023, Номер 5(4), С. 1680 - 1716

Опубликована: Ноя. 20, 2023

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present comprehensive analysis of YOLO’s evolution, examining the innovations contributions in each iteration from original up to YOLOv8, YOLO-NAS, with transformers. start by describing standard metrics postprocessing; then, we discuss major changes network architecture training tricks model. Finally, summarize essential lessons development provide perspective on its future, highlighting potential research directions enhance systems.

Язык: Английский

Процитировано

959
DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism DOI

Arunabha M. Roy,

Jayabrata Bhaduri

Advanced Engineering Informatics, Год журнала: 2023, Номер 56, С. 102007 - 102007

Опубликована: Апрель 1, 2023

Язык: Английский

Процитировано

164
A novel deep learning-based approach for malware detection DOI
Kamran Shaukat, Suhuai Luo, Vijay Varadharajan

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 122, С. 106030 - 106030

Опубликована: Март 9, 2023

Язык: Английский

Процитировано

119
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO DOI Creative Commons
Muhammad Hussain

IEEE Access, Год журнала: 2024, Номер 12, С. 42816 - 42833

Опубликована: Янв. 1, 2024

This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing thorough understanding structural components. Subsequently, highlights key innovations introduced in each variant, shedding light on incremental refinements. The includes benchmarked performance metrics, offering quantitative measure variant's capabilities. further presents variants across diverse range domains, manifesting their real-world impact. structured ensures comprehensive examination YOLOs journey, methodically communicating advancements and before delving into domain applications. It envisioned, incorporation concepts such as federated learning can introduce collaborative training paradigm, where models benefit from multiple edge devices, enhancing privacy, adaptability, generalisation.

Язык: Английский

Процитировано

102
An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT) DOI
Sonain Jamil,

Arunabha M. Roy

Computers in Biology and Medicine, Год журнала: 2023, Номер 158, С. 106734 - 106734

Опубликована: Март 1, 2023

Язык: Английский

Процитировано

66
Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity DOI

Arunabha M. Roy,

Rikhi Bose,

Veera Sundararaghavan

и другие.

Neural Networks, Год журнала: 2023, Номер 162, С. 472 - 489

Опубликована: Март 13, 2023

Язык: Английский

Процитировано

59
A data-driven physics-constrained deep learning computational framework for solving von Mises plasticity DOI

Arunabha M. Roy,

Suman Guha

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 122, С. 106049 - 106049

Опубликована: Март 7, 2023

Язык: Английский

Процитировано

45
An Improved YOLOv8 to Detect Moving Objects DOI Creative Commons

Mukaram Safaldin,

Nizar Zaghden, Mahmoud Mejdoub

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 59782 - 59806

Опубликована: Янв. 1, 2024

Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects visual streams presents distinct challenges. This paper proposes a refined YOLOv8 detection model, emphasizing motion-specific detections varied contexts. Through tailored preprocessing and architectural adjustments, we heighten the model's sensitivity to movements. Rigorous testing against KITTI, LASIESTA, PESMOD, MOCS benchmark datasets revealed that modified outperforms state-of-the-art models, especially environments significant movement. Specifically, our model achieved an accuracy of 90%, mean Average Precision (mAP) maintained processing speed 30 frames per second (FPS), Intersection over Union (IoU) score 80%. offers detailed insight into trajectories, proving invaluable areas like security, traffic management, film analysis where motion understanding is critical. As importance dynamic scene interpretation grows artificial intelligence computer vision, proposed enhanced highlights potential specialized underscores significance findings evolving field detection.

Язык: Английский

Процитировано

41
Detection of Soybean Insect Pest and a Forecasting Platform Using Deep Learning with Unmanned Ground Vehicles DOI Creative Commons

Yu-Hyeon Park,

Sung Hoon Choi,

Yeon-Ju Kwon

и другие.

Agronomy, Год журнала: 2023, Номер 13(2), С. 477 - 477

Опубликована: Фев. 6, 2023

Soybeans (Glycine max (L.) Merr.), a popular food resource worldwide, have various uses throughout the industry, from everyday foods and health functional to cosmetics. are vulnerable pests such as stink bugs, beetles, mites, moths, which reduce yields. Riptortus pedestris (R. pedestris) has been reported cause damage pods leaves soybean growing season. In this study, an experiment was conducted detect R. according three different environmental conditions (pod filling stage, maturity artificial cage) by developing surveillance platform based on unmanned ground vehicle (UGV) GoPro CAM. Deep learning technology (MRCNN, YOLOv3, Detectron2)-based models used in can be quickly challenged (i.e., built with lightweight parameter) immediately through web application. The image dataset distributed random selection for training, validation, testing then preprocessed labeling annotation. deep model localized classified individuals bounding box masking data. achieved high performances, at 0.952, 0.716, 0.873, respectively, represented calculated means of average precision (mAP) value. manufactured will enable identification field effective tool insect forecasting early stage pest outbreaks crop production.

Язык: Английский

Процитировано

28
Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics DOI Creative Commons
Mohammed Razzok, Abdelmajid Badri, Ilham El Mourabit

и другие.

Information, Год журнала: 2023, Номер 14(4), С. 218 - 218

Опубликована: Апрель 3, 2023

Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic commercial potential. Their objective is locate various pedestrians in videos assign them unique identities. The data association task problematic, particularly when dealing with inter-pedestrian occlusion. This occurs multiple cross paths or move too close together, making it difficult for the system identify track individual pedestrians. Inaccurate can lead false alarms, missed detections, incorrect decisions. To overcome this challenge, our paper focuses on improving pedestrian system’s Deep-SORT algorithm, which solved as a linear optimization problem using newly generated cost matrix. We introduce set new matrices that rely metrics such intersections, distances, bounding boxes. evaluate trackers real time, we use YOLOv5 images. also perform experimental evaluations Multiple Object Tracking 17 (MOT17) challenge dataset. proposed demonstrate promising results, showing an improvement most MOT performance compared default intersection over union (IOU)

Язык: Английский

Процитировано

25