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A method for custom measurement of fish dimensions using the improved YOLOv5-keypoint framework with multi-attention mechanisms

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Water Biology and Security 2024年第4期3卷 130-142页

作者： Danying Cao Cheng Guo Mijuan Shi Yuhang Liu Yutong Fang Hong Yang Yingyin Cheng Wanting Zhang Yaping Wang Yongming Li Xiao-Qin Xia Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture(CAS) Hubei Hongshan LaboratoryKey Laboratory of Aquaculture Disease ControlMinistry of Agriculture and Rural AffairsThe Innovation Academy of Seed DesignInstitute of HydrobiologyChinese Academy of SciencesWuhan 430072China College of Advanced Agricultural Sciences University of Chinese Academy of SciencesBeijing 100101China College of Fisheries and Life Science Dalian Ocean UniversityDalian116023China

Dimensional data directly reflects the growth rate of individual fish,an important economic trait of interest to fish *** obtaining large-scale fish dimension data would be valuable for both selective breeding and *** address this,our study proposes a custom dimension measurement method for fish using the YOLOv5-keypoint framework with multi-attention *** optimized the YOLOv5 framework,incorporated the SimAM attention mechanism to achieve more accurate and faster fish detection,and added customizable landmarks to the network structure,enabling flexible configuration of the number and location of feature points in the training *** method is applicable to various aquacultural species and other *** tested the effectiveness of the method using the economically important grass carp(Ctenopharyngodon idella).The proposed method outperforms pure YOLOv5,Faster R-CNN,and SSD in terms of precision and recall rates,achieving an impressive average precision of ***,field trials confirmed the method's exceptional measurement accuracy,exceeding 97%compatibility with manual measurements,while demonstrating a realtime speed of 38 frames per second on the NVIDIA RTX *** enables efficient and accurate large-scale surface dimension measurements of economic *** facilitate massive measurements in agricultural research,we have implemented this method as an online platform,called Mode-recognition Ruler(MrRuler,http://bioinf ***/mrruler).The platform identifies objects in a single image at an average speed of 0.486±0.005 s,based on a dataset of 10,000 *** includes two preset carp models and allows users to upload training datasets for custom models of their targets of interest.

关键词： Computer vision planar dimensions measurement Attention mechanisms Aquaculture

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