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FARNET: FARMING ACTION RECOGNITION FROM VIDEOS BASED ON COORDINATE ATTENTION AND YOLOV7-TINY NETWORK IN AQUACULTURE

文献类型： 外文期刊

作者： Yang, Xinting 1 ; Pan, Liang 1 ; Wang, Dinghong 2 ; Zeng, Yuhao 2 ; Zhu, Wentao 1 ; Jiao, Dongxiang 3 ; Sun, Zhenlong 3 ; Sun, Chuanheng 4 ; Zhou, Chao 2 ;

作者机构： 1.Shanghai Ocean Univ, Coll Informat, Shanghai, Peoples R China

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

3.Zhong Yang Fishery, Jiangmen, Peoples R China

4.Intelligent Syst, Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Action detection; Applying pesticides; Coordinate attention; FARnet; Inspection

期刊名称：JOURNAL OF THE ASABE

ISSN： 2769-3295

年卷期： 2023 年 66 卷 4 期

页码：

收录情况： SCI

摘要： In aquaculture, regular "inspection" and "applying pesticides" are essential to improving production efficiency and fish disease treatment, but the current aquaculture system does not effectively support these strategies. Therefore, this paper proposes a farming action recognition network (FARnet), which can accurately locate the farmers in the video and detect the actions of "applying pesticides" and "inspection." The dataset was captured and produced by multi-angle cameras, which were consulted with relevant experts. In this network, Coordinate Attention (CA) was used to improve the Efficient Layer Aggregation Networks-tiny (ELAN-tiny) and Spatial Pyramid Pooling (SPP) structures in the YOLOv7-tiny network. The precise implementation methods are as follows: (1) The convolution in ELAN-tiny was replaced with the CA module, and a shortcut was added. (2) A CA module was added to the final layer of the Spatial Pyramid Pooling (SPP) module. (3) The improved Efficient Layer Aggregation Networks-Coordinate Attention (ELAN-CA) and Spatial Pyramid Pooling-Coordinate Attention (SPP-CA) were used to extract action features and perform feature correction by ADD (Feature fusion by feature map summation) in the backbone. The results demonstrated that the FARnet achieved significantly better detection results than the YOLOv7-tiny network, where mAP@.5 improved by 0.1% from 99.4% to 99.5%, and the mAP@.5:.95 improved by 6.6% from 78.2% to 84.8%. Therefore, the FARnet can effectively detect and identify the "inspection" and "applying pesticides" actions of farmers and provide useful input information for the intelligent management system.

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