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Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult Rhynchophorus ferrugineus

文献类型： 外文期刊

作者： Wu, Shuai 1 ; Wang, Jianping 1 ; Liu, Li 2 ; Chen, Danyang 1 ; Lu, Huimin 1 ; Xu, Chao 1 ; Hao, Rui 1 ; Li, Zhao 1 ; Wang, Qingxuan 1 ;

作者机构： 1.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China

2.Chinese Acad Trop Agr Sci, Coconut Res Inst, Hainan Key Lab Trop Oil Crops Biol, Wenchang 571339, Peoples R China

3.Univ Sci & Technol Beijing, Shunde Innovat Sch, Foshan 528399, Peoples R China

关键词： red palm weevil; YOLOv5; attention mechanism; detection

期刊名称：INSECTS （ 影响因子：3.0； 五年影响因子：3.1 ）

ISSN：

年卷期： 2023 年 14 卷 8 期

页码：

收录情况： SCI

摘要： The red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs' management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests.

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