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陈科, 周勇, 薛明洋, 朱松明, 赵建, 蔡海莺, 叶章颖. 基于机器视觉和改进YOLOv5s的鲫病害轻量级无损检测模型[J]. 水生生物学报. DOI: 10.7541/2024.2023.0226
引用本文: 陈科, 周勇, 薛明洋, 朱松明, 赵建, 蔡海莺, 叶章颖. 基于机器视觉和改进YOLOv5s的鲫病害轻量级无损检测模型[J]. 水生生物学报. DOI: 10.7541/2024.2023.0226
CHEN Ke, ZHOU Yong, XUE Ming-Yang, ZHU Song-Ming, ZHAO Jian, CAI Hai-Ying, YE Zhang-Ying. LIGHTWEIGHT NONDESTRUCTIVE DETECTION MODEL OF CRUCIAN CARP DISEASE BASED ON MACHINE VISION AND IMPROVED YOLOV5S[J]. ACTA HYDROBIOLOGICA SINICA. DOI: 10.7541/2024.2023.0226
Citation: CHEN Ke, ZHOU Yong, XUE Ming-Yang, ZHU Song-Ming, ZHAO Jian, CAI Hai-Ying, YE Zhang-Ying. LIGHTWEIGHT NONDESTRUCTIVE DETECTION MODEL OF CRUCIAN CARP DISEASE BASED ON MACHINE VISION AND IMPROVED YOLOV5S[J]. ACTA HYDROBIOLOGICA SINICA. DOI: 10.7541/2024.2023.0226

基于机器视觉和改进YOLOv5s的鲫病害轻量级无损检测模型

LIGHTWEIGHT NONDESTRUCTIVE DETECTION MODEL OF CRUCIAN CARP DISEASE BASED ON MACHINE VISION AND IMPROVED YOLOV5S

  • 摘要: 文章以鲫常见病害为例, 从实际生产角度出发, 提出了一种基于机器视觉和改进YOLOv5s的鲫病害轻量级无损检测模型, 可实现鲫鱼体多种病害的同步无损快速检测。首先, 通过利用ShuffleNetV2替换YOLOv5s主干网络, 对模型进行轻量化改进; 在此基础上, 耦合一种基于卷积块的注意力机制Convolutional block attention module (CBAM)提高模型精准度; 最后, 结合空洞空间卷积池化金字塔Atrous spatial pyramid pooling (ASPP)提升模型鲁棒性。通过在自制鲫病害数据集上测试可知, 文章所提出模型病害检测精度可达92.0%, 模型体积仅为14400 kb, 优于当前相关主流模型(最高精确率为83.6%, 最小体积为15750 kb), 为水产养殖鱼类病害无损快速检测提供了技术支撑。

     

    Abstract: In this study, we present a lightweight, nondestructive testing model for diagnosing common diseases in crucian carp as an example. This model, based on an improved YOLOv5s architecture, aims to achieve simultaneous, nondestructive and rapid detection of multiple diseases in crucian carp, addressing the practical needs of the industry. The proposed model is designed to be lightweight and is enhanced by replacing the YOLOv5s backbone network with ShuffleNetV2. Additionally, it incorporates a convolutional block attention module (CBAM) to enhance model accuracy. Furthermore, the model is combined with the cavity space convolutional pooling pyramid (ASPP) to improve model robustness. Through testing on the homemade crucian carp disease dataset, it can be seen that the disease detection accuracy of the model proposed can reach 92.0%, and the volume of the model is only 14400 kb, which is better than the current mainstream model (the highest accuracy is 83.6%, and the smallest volume is 15750 kb), which provides technical support for the non-destructive and rapid detection of fish diseases in aquaculture.

     

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