基于坐标关注和YOLOv7-tiny网络的水产养殖视频养殖动作识别

IF 1.2 4区 农林科学 Q3 AGRICULTURAL ENGINEERING
Xinting Yang, Liang Pan, Dinghong Wang, Yuhao Zeng, Wentao Zhu, Dongxiang Jiao, Zhenlong Sun, Chuanheng Sun, Chao Zhou
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引用次数: 0

摘要

重点实现了视频中农业动作的自动检测与识别。YOLOv7-tiny通过结合协调注意(CA)得到增强。性能指标mAP@.5、mAP@.5:。95项分别上升0.1%和6.6%。本发明提供了一种检测“检验”和“施药”的智能方法。摘要在水产养殖中,定期“检查”和“施用农药”对于提高生产效率和鱼病治疗至关重要,但目前的水产养殖系统并未有效支持这些策略。因此,本文提出了一种农业动作识别网络(FARnet),该网络可以准确定位视频中的农民,并检测出“施药”和“检查”的动作。该数据集由多角度摄像机捕获并生成,并咨询了相关专家。在该网络中,利用CA (Coordinate Attention)对YOLOv7-tiny网络中的高效层聚集网络-tiny (ELAN-tiny)和空间金字塔池(SPP)结构进行了改进。具体实现方法如下:(1)将ELAN-tiny中的卷积替换为CA模块,并增加一个快捷方式。(2)在空间金字塔池(SPP)模块的最后一层增加一个CA模块。(3)采用改进的高效层聚集网络-坐标注意(ELAN-CA)和空间金字塔池-坐标注意(SPP-CA)提取动作特征,并在骨干网络中进行ADD (feature fusion by feature map summation)特征校正。结果表明,FARnet的检测结果明显优于YOLOv7-tiny网络,其中mAP@.5 .从99.4%提高到99.5%,提高了0.1%,mAP@.5:。95从78.2%提高到84.8%,提高了6.6%。因此,FARnet可以有效地检测和识别农民的“检查”和“施药”行为,为智能管理系统提供有用的输入信息。关键词:动作检测;施药;协调注意;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FARnet: Farming Action Recognition From Videos Based on Coordinate Attention and YOLOv7-tiny Network in Aquaculture
Highlights The automatic detection and recognition of farming action in video are realized. The YOLOv7-tiny was enhanced by incorporating Coordinate Attention (CA). The performance indices mAP@.5 and mAP@.5:.95 improved by 0.1% and 6.6%, respectively. An intelligent method for detecting "inspection" and "applying pesticides" is provided. Abstract. 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. Keywords: Action detection, Applying pesticides, Coordinate attention, FARnet, Inspection.
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