YOLO-LCE: A Lightweight YOLOv8 Model for Agricultural Pest Detection

Abstract

Agricultural pest detection through image analysis is a key technology in automated pest-monitoring systems. However, some existing pest detection models face excessive model complexity. This study proposes YOLO-LCE, a lightweight model based on the YOLOv8 architecture for agricultural pest detection. Firstly, a Lightweight Complementary Residual (LCR) module is proposed to extract complementary features through a dual-branch structure. It enhances detection performance and reduces model complexity. Additionally, Efficient Partial Convolution (EPConv) is proposed as a downsampling operator. It adopts an asymmetric channel splitting strategy to efficiently utilize features. Furthermore, the Ghost module is introduced to the detection head to reduce computational overhead. Finally, WIoUv3 is used to improve detection performance further. YOLO-LCE is evaluated on the Pest24 dataset. Compared to the baseline model, YOLO-LCE achieves mAP50 improvement of 1.7 percentage points, mAP50-95 improvement of 0.4 percentage points, and precision improvement of 0.5 percentage points. For computational efficiency, parameters are reduced by 43.9%, and GFLOPs are reduced by 33.3%. These metrics demonstrate that YOLO-LCE improves detection accuracy while reducing computational complexity, providing an effective solution for lightweight pest detection.

Topics & Concepts