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Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network

文献类型： 外文期刊

作者： Hu, Xuelong 1 ; Liu, Yang 1 ; Zhao, Zhengxi 2 ; Liu, Jintao 2 ; Yang, Xinting 2 ; Sun, Chuanheng 2 ; Chen, Shuhan 1 ; Li, 1 ;

作者机构： 1.Yangzhou Univ, Sch Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Natl Engn Lab Agri Prod Qual Traceabil, Beijing 100097, Peoples R China

5.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

6.Univ Almeria, Almeria 04120, Spain

关键词： Aquaculture; Improved YOLO-V4 network; Underwater object detection; Uneaten feed pellets; Deep learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 185 卷

页码：

收录情况： SCI

摘要： In aquaculture, the real-time detection and monitoring of feed pellet consumption is an important basis for formulating scientific feeding strategies that can effectively reduce feed waste and water pollution, which is a win-win scenario in terms of economic and ecological benefits. However, low-quality underwater images and extremely small targets present great challenges to feed pellet detection. To overcome these challenges, this paper proposes an uneaten feed pellet detection model using an improved You Only Look Once (YOLO)-V4 network for aquaculture. The specific implementation methods are as follows: (1) The feature map responsible for large-scale information in the original YOLO-V4 network is replaced by a finer-grained YOLO feature map by modifying the connection mode of the feature pyramid network (FPN) + path aggregation network (PANet). (2) The residual connection mode in CSPDarknets is modified via a DenseNet, which further improves the feature reuse and the network performance. (3) Finally, a de-redundancy operation is carried out to reduce the complexity of the YOLO-V4 network while ensuring the detection accuracy. Experimental results in a real fish farm showed that the detection accuracy is better than that of the original YOLO-V4 network, and the average precision is improved from 65.40% to 92.61% (when the intersection over union is 0.5), for an increase of 27.21%. Additionally, the amount of computation is reduced by approximately 30%. Therefore, the improved YOLO-V4 network can effectively detect underwater feed pellets and is applicable in actual aquaculture environments.

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