YOLO-CFM: Improved YOLOv5 for Vehicle Detection in Drone-captured Infrared Images

Zhang, Yong; Zhang, Yanbo; Gu, Fengyang; Chen, Mingyue; Zhu, Hui

doi:10.1145/3578339.3578340

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Peng Zhang, Weimin Lei, Xinlei Zhao, Lijia Dong and Zhaonan Lin, [3] presents crowd counting is a crucial task in fields like video surveillance, accident prediction, public security, and intelligent transportation. However, it faces challenges such as large-scale crowd aggregation in public places, positioning errors in large-scale datasets, and inconsistent human head target size in dense images.…”

Section: Related Workmentioning

confidence: 99%

AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review

Francis

2024

IJSREM

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This paper presents a groundbreaking approach to data collection in hazardous or inaccessible environments, presenting the design, development, and implementation of an innovative autonomous robot. The robot is designed to navigate and collect valuable data from locations too dangerous or remote for human exploration, enabling scientific research and exploration in unprecedented ways. The AI-powered drone is equipped for precise human identification, controlled through a user-friendly mobile app. The software analyzes live drone footage to detect human presence using models like YOLO, with high accuracy in real-time human detection tasks. The robot is equipped with an array of sensors, including cameras, and uses image processing technology for processing images. GPS tracking technology is used for device tracking. The proposed autonomous robot promises to revolutionize data collection in unreachable environments, opening new avenues for scientific discovery, resource assessment, and environmental monitoring. Key Words: YOLOV8, UAV, Python, Flask, Computer vision, AI.

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Section: Related Workmentioning

confidence: 99%

AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review

Francis

2024

IJSREM

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Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Vo,

Ngo,

et al. 2024

IEEE Access

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The development of technological devices, such as satellites and drones, has made it easier to collect images and videos from above. With rich and diverse data sources, the problem of detecting objects in aerial images is formed to serve real-life situations: rescue missions, social scenarios, security needs, and more. This issue attracts a lot of attention from researchers in computer vision because of its high applicability in real life. However, object detection in images collected from drones faces many significant challenges due to the complex factors of real-life environments. To overcome these difficulties, many studies focus on developing effective models for detecting and tracking objects from UAV systems. In this paper, our primary emphasis is comprehensive survey of methodologies pertaining to horizontal and oriented object detection. We analyzed and compared existing approaches such as CNN-based vs. Transformerbased, Anchor-based vs. Anchor-free. Moreover, we also learned about ways to represent objects using bounding boxes in oriented object detection. After researching many practical and innovative techniques in different approaches, we have carefully selected 15 methods for horizontal object detection and 18 for oriented object detection that were recently researched and developed to conduct classifications, surveys and assessments. We also provide a taxonomy mind map to assist readers in gaining an overview of the methods we surveyed. Additional, we examine 17 aerial image datasets collected from platforms and in various locations worldwide. These are typical datasets used to serve two primary tasks: Horizontal Object Detection and Oriented Object Detection. Then, we compared the experimental resultsof the previously introduced methods on four typical datasets: VisDrone, UAVDT, DOTAv1.0, and HRSC2016. The above datasets present diversity in the number of classes and the complexity of real-life conditions. Furthermore, they have been tested with various methods and shown remarkable results. For the reasons mentioned, we have enough basis to confirm that the comparison results are objective so that other research groups can make accurate comments and assessments on experimental methods on each dataset. We believe that updating and synthesizing the latest research will provide complete and comprehensive information, helping researchers to have more survey material for the development of the field of object detection in the future.INDEX TERMS aerial dataset, aerial image object detection, oriented object detection, and horizontal object detection.

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Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance

Aibibu,

Lan,

Zeng

et al. 2024

Drones

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With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

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YOLO-CFM: Improved YOLOv5 for Vehicle Detection in Drone-captured Infrared Images

Cited by 3 publications

References 6 publications

AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review

AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review

Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance

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