Real-time nondestructive fish behavior detecting in mixed polyculture system using deep-learning and low-cost devices

文献类型： 外文期刊

第一作者： Hu, Jun

作者： Hu, Jun;Zhao, Dandan;Chen, Wenxuan;Zhou, Chengquan;Zhang, Yanfeng;Zhou, Chengquan;Zhang, Yanfeng

作者机构：

关键词： Fish behavior detecting; YOLO network; Low-cost imaging system; Smart fish farming; Mixed polyculture system

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：6.954； 五年影响因子：6.789 ）

ISSN： 0957-4174

年卷期： 2021 年 178 卷

页码：

收录情况： SCI

摘要： Fish behavior has attracted increasing attention in global aquaculture because it provides important information about productivity and fish quality. The use of images to detect fish behavior has shown potential in aquaculture behavioral studies by providing higher spatial resolution, efficiency, and accuracy than conventional approaches such as manual measurement. In addition, it allows for more quantitative data analysis than do other methods. To date, conventional image processing approaches to monitor fish behavior have been based primarily on appearance, morphology, and color information. This approach is complex and/or time-consuming and limits the practicality of such methods in aquaculture. To address these problems, we present herein a noninvasive, rapid, low-cost procedure based on an underwater imaging system and a deep learning framework to detect fish behavior with high accuracy in a mixed polyculture system. The specific objectives of this study are (1) to design a low-cost underwater imaging system that can describe and quantify fish behavior via visual images, and (2) to develop a lightweight deep learning structure to rapidly and accurately detect fish behavior under various conditions. Toward this end, images of fish are first captured via a low-cost imaging system, following which they are preprocessed to reduce noise and enhance data information. Finally, an improved You Only Look Once version 3 Lite (YOLOv3-Lite) network with a novel backbone structure is used to improve the pooling block and loss function and thereby better recognize fish behavior. The proposed method was tested on a real dataset and produced a Precision of 0.897, a Recall of 0.884, an intersection over union of 0.892, and 240 frames per second. Furthermore, when compared with faster region-convolutional neural network, YOLO, YOLOv2, YOLOv3, and single shot multi-Box detector, the performance of each evaluation metric of the proposed method was improved by 10%-20%. This comprehensive analysis indicates that the proposed method provides state-of-the-art performance and may be used in fish farms.

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