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

Abstract

The development of the agricultural economy is hindered by various pest-related problems. Most pest detection 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 algorithm are higher than those of the YOLOX detection model by 5.1% and 6.2%, respectively.