Open Access

ARTICLE

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

Bing Shi^1,*, Jianhua Zhao¹, Bin Ma¹, Juan Huan², Yueping Sun³

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

* Corresponding Author: Bing Shi. Email:

Computers, Materials & Continua 2024, 81(2), 2437-2456. https://doi.org/10.32604/cmc.2024.056377

Received 21 July 2024; Accepted 11 September 2024; Issue published 18 November 2024

Abstract

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 the α-SIoU loss function, which combines the α-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.

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Keywords

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