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      A detection method of the rescue targets in the marine casualty based on improved YOLOv5s


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          In recent years, with the deep exploitation of marine resources and the development of maritime transportation, ship collision accidents occur frequently, which leads to the increasingly heavy task of maritime Search and Rescue (SAR). Unmanned Aerial Vehicles (UAVs) have the advantages of flexible maneuvering, robust adaptability and extensive monitoring, which have become an essential means and tool for emergency rescue of maritime accidents. However, the current UAVs-based drowning people detection technology has insufficient detection ability and low precision for small targets in high-altitude images. Moreover, limited by the load capacity, UAVs do not have enough computing power and storage space, resulting in the existing object detection algorithms based on deep learning cannot be directly deployed on UAVs. To solve the two issues mentioned above, this paper proposes a lightweight deep learning detection model based on YOLOv5s, which is used in the SAR task of drowning people of UAVs at sea. First, an extended small object detection layer is added to improve the detection effect of small objects, including the extraction of shallow features, a new feature fusion layer and one more prediction head. Then, the Ghost module and the C3Ghost module are used to replace the Conv module and the C3 module in YOLOv5s, which enable lightweight network improvements that make the model more suitable for deployment on UAVs. The experimental results indicate that the improved model can effectively identify the rescue targets in the marine casualty. Specifically, compared with the original YOLOv5s, the improved model mAP@0.5 value increased by 2.3% and the mAP@0.5:0.95 value increased by 1.1%. Meanwhile, the improved model meets the needs of the lightweight model. Specifically, compared with the original YOLOv5s, the parameters decreased by 44.9%, the model weight size compressed by 39.4%, and Floating Point Operations (FLOPs) reduced by 22.8%.

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          ImageNet classification with deep convolutional neural networks

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            Microsoft COCO: Common Objects in Context

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              You Only Look Once: Unified, Real-Time Object Detection


                Author and article information

                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                16 November 2022
                : 16
                : 1053124
                School of Marine Engineering Equipment, Zhejiang Ocean University , Zhoushan, China
                Author notes

                Edited by: Qiang Zhang, Shandong Jiaotong University, China

                Reviewed by: Jiangyuan Mei, Midea Group, China; Wenlin Wang, Wuhan University of Technology, China; Qingzhong Cai, Beihang University, China

                *Correspondence: Shujie Yang, shujie.yang@ 123456zjou.edu.cn
                Copyright © 2022 Bai, Dai, Wang and Yang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 25 September 2022
                : 31 October 2022
                Page count
                Figures: 16, Tables: 7, Equations: 14, References: 23, Pages: 14, Words: 6064
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;

                yolov5s,small object detection,unmanned aerial vehicles (uavs),lightweight mode,marine search and rescue (sar)


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