An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms

doi:10.3390/jmse10091230

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms
	Li, Yan 3,4,5; Bai, Xinying 2,3,4,5; Xia, Chunlei 1
刊名	JOURNAL OF MARINE SCIENCE AND ENGINEERING
	2022-09-01
卷号	10 期号:9 页码:13
关键词	marine organism target identification deep learning attention mechanism model optimization
DOI	10.3390/jmse10091230
英文摘要	Machine vision-based automatic detection of marine organisms is a fundamental task for the effective analysis of production and habitat changes in marine ranches. However, challenges of underwater imaging, such as blurring, image degradation, scale variation of marine organisms, and background complexity, have limited the performance of image recognition. To overcome these issues, underwater object detection is implemented by an improved YOLOV5 with an attention mechanism and multiple-scale detection strategies for detecting four types of common marine organisms in the natural scene. An image enhancement module is employed to improve the image quality and extend the observation range. Subsequently, a triplet attention mechanism is introduced to the YOLOV5 model to improve the feature extraction ability. Moreover, the structure of the prediction head of YOLOV5 is optimized to capture small-sized objects. Ablation studies are conducted to analyze and validate the effective performance of each module. Moreover, performance evaluation results demonstrate that our proposed marine organism detection model is superior to the state-of-the-art models in both accuracy and speed. Furthermore, the proposed model is deployed on an embedded device and its processing time is less than 1 s. These results show that the proposed model has the potential for real-time observation by mobile platforms or undersea equipment.
资助项目	National Natural Science Foundation of China[61821005] ; Liaoning Provincial Natural Science Foundation of China[2020-MS-031] ; State Key Laboratory of Robotics at Shenyang Institute of Automation[2021-Z10L01] ; Liaoning Revitalization Talents Program[XLYC2007035] ; Liaoning Revitalization Talents Program[XLYC1902032]
WOS关键词	NETWORK
WOS研究方向	Engineering ; Oceanography
语种	英语
出版者	MDPI
WOS记录号	WOS:000857628800001
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/31696]
专题	烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者	Li, Yan; Xia, Chunlei
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 2.Liaoning Univ, Coll Informat, Shenyang 110136, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Mfg, Shenyang 110169, Peoples R China 4.Chinese Acad Sci, Inst Robot, Shenyang 110169, Peoples R China 5.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Li, Yan,Bai, Xinying,Xia, Chunlei. An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2022,10(9):13.
APA	Li, Yan,Bai, Xinying,&Xia, Chunlei.(2022).An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms.JOURNAL OF MARINE SCIENCE AND ENGINEERING,10(9),13.
MLA	Li, Yan,et al."An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms".JOURNAL OF MARINE SCIENCE AND ENGINEERING 10.9(2022):13.