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

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 

机构地区：[1]Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture(CAS),Hubei Hongshan Laboratory,Key Laboratory of Aquaculture Disease Control,Ministry of Agriculture and Rural Affairs,The Innovation Academy of Seed Design,Institute of Hydrobiology,Chinese Academy of Sciences,Wuhan 430072,China [2]College of Advanced Agricultural Sciences,University of Chinese Academy of Sciences,Beijing 100101,China [3]College of Fisheries and Life Science,Dalian Ocean University,Dalian,116023,China

出　　处：《Water Biology and Security》2024年第4期130-142,共13页水生生物与安全（英文）

基　　金：supported by the National Key R&D Program of China[grant number 2021YFD1200804];the Strategic Priority Research Program of the Chinese Academy of Sciences[Precision Seed Design and Breeding,grant number XDA24010206].

摘　　要：Dimensional data directly reflects the growth rate of individual fish,an important economic trait of interest to fish researchers.Efficiently obtaining large-scale fish dimension data would be valuable for both selective breeding and production.To address this,our study proposes a custom dimension measurement method for fish using the YOLOv5-keypoint framework with multi-attention mechanisms.We 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 dataset.This method is applicable to various aquacultural species and other objects.We 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 0.9781.Notably,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 A4000.This enables efficient and accurate large-scale surface dimension measurements of economic fish.To facilitate massive measurements in agricultural research,we have implemented this method as an online platform,called Mode-recognition Ruler(MrRuler,http://bioinf o.ihb.ac.cn/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 images.MrRuler 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 

分 类 号：Q95[生物学—动物学]