A Novel YOLOv5s-Based Lightweight Model for Detecting Fish’s Unhealthy States in Aquaculture  

作　　者：Bing Shi Jianhua Zhao Bin Ma Juan Huan Yueping Sun 

机构地区：[1]School of Mechanical Engineering and Rail Transit,Changzhou University,Changzhou,213164,China [2]School of Microelectronics and Control Engineering,Changzhou University,Changzhou,213164,China [3]School of Electrical and Information Engineering,Jiangsu University,Zhenjiang,212013,China

出　　处：《Computers, Materials & Continua》2024年第11期2437-2456,共20页计算机、材料和连续体（英文）

基　　金：supported by The Agricultural Science and Technology Independent Innovation Fund Project of Jiangsu Province(CX(22)3111);the National Natural Science Foundation of China Project(62173162);partly by the Changzhou Science and Technology Support Project(CE20225016).

摘　　要：Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.

关 键 词：Intensive recirculating aquaculture unhealthy fish detection improved YOLOv5s lightweight structure 

分 类 号：S951.2[农业科学—水产养殖]