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Journal of Chinese Agricultural Mechanization

Journal of Chinese Agricultural Mechanization ›› 2024, Vol. 45 ›› Issue (5): 195-201.DOI: 10.13733/j.jcam.issn.2095-5553.2024.05.030

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Dead chicken target detection algorithm based on lightweight YOLOv4

Qi Haixia1, 2, 3, Li Chengjie1, Huang Guizhen1   

  • Online:2024-05-15 Published:2024-05-22

基于轻量化YOLOv4的死淘鸡目标检测算法

漆海霞1, 2, 3,李承杰1,黄桂珍1   

  • 基金资助:
    广州市科技项目(20212100026)

Abstract: Aiming at the problems that there are few studies on dead chicken target detection and the large size of the highprecision detection algorithm makes it difficult to deploy to mobile devices, a lightweight dead chicken target detection algorithm based on YOLOv4 is proposed. Firstly, the team collects images of dead chickens in cages from largescale egg production plants to build a target detection dataset. Then, MobileNetv3 backbone extraction network with depthseparable convolution is introduced in the algorithm to reduce the model size. Finally, a selfattentive mechanism module is added before the maximum pooling layer to enhance the algorithms capture of global semantic information. Experimental results in a selfbuilt dataset show that the improved algorithm has higher accuracy in the dead pheasant target detection task, with mAP values and recall rates of 97.74% and 98.15% respectively. The model size is reduced to 1/5 of the original algorithm, and the frame rate reaches 77 frames/s under GPU acceleration, doubling the detection speed and meeting the requirements of embedded deployments.

Key words: identification of dead chicken, deep learning, lightweight network, MobileNet, deep separable convolution

摘要: 针对目前死淘鸡目标检测研究较少,高精度检测算法体积大难以部署至移动式设备等问题,提出一种基于YOLOv4的轻量化死淘鸡目标检测算法。采集大规模蛋鸡养殖工厂笼中死淘鸡图片,建立目标检测数据集;在算法中引入MobileNetv3主干提取网络与深度可分离卷积来降低模型体积;并在最大池化层前添加自注意力机制模块,增强算法对全局语义信息的捕获。在自建数据集中的试验结果表明,改进算法在死淘鸡目标检测任务中有更高的准确度,其mAP值与召回率分别达到97.74%和98.15%,模型大小缩小至原算法的1/5,在GPU加速下帧数达到77帧/s,检测速度提高1倍,能够满足嵌入式部署需求。

关键词: 死淘鸡识别, 深度学习, 轻量化网络, MobileNet, 深度可分离卷积

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