Pests Identification of IP102 by YOLOv5 Embedded with the Novel Lightweight Module

Pests Identification of IP102 by YOLOv5 Embedded with the Novel Lightweight Module

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The development of the agricultural economy is hindered by various pest-related problems.Most pest detection Shirts studies only focus on a single pest category, which is not suitable for practical application scenarios.This paper presents a deep learning algorithm based on YOLOv5, which aims to assist agricultural workers in efficiently diagnosing information related to 102 types of pests.To achieve this, we propose a new lightweight convolutional module called C3M, which is inspired by the MobileNetV3 network.Compared to the original convolution module C3, C3M occupies less computing memory and results in a faster inference speed, with the detection precision improved by 4.

6%.In addition, the GAM (Global Attention Mechanism) is introduced into the neck of YOLO5, which further improves the detection capability of the model.The experimental results indicate that the C3M-YOLO algorithm performs better than YOLOv5 on IP102, a public dataset consisting of 102 pests.Specifically, the detection precision P is 2.4% higher than that of the original model, and mAP0.

75 increased by 1.7%, while the F1-score improved by 1.8%.Furthermore, the mAP0.5 and mAP0.

75 of the C3M-YOLO Powerline/Ethernet Converters algorithm are higher than those of the YOLOX detection model by 5.1% and 6.2%, respectively.