A Lightweight and Dynamic Feature Aggregation Method for Cotton Field Weed Detection Based on Enhanced YOLOv8

Abstract

Weed detection is closely related to agricultural production, but often faces the problems of leaf shading and limited computational resources. Therefore, this study proposes an improved weed detection algorithm based on YOLOv8. Firstly, the Dilated Feature Integration Block is designed to improve the feature extraction in the backbone network by introducing large kernel convolution and multi-scale dilation convolution, which utilizes information from different scales and levels. Secondly, to solve the problem of a large number of parameters in the feature fusion process of the Path Aggregation Feature Pyramid Network, a new feature fusion architecture multi-scale feature interaction network is designed, which achieves the high-level semantic information to guide the low-level semantic information through the attention mechanism. Finally, we propose a Dynamic Feature Aggregation Head to solve the problem that the YOLOv8 detection head cannot dynamically focus on important features. Comprehensive experiments on two publicly accessible datasets show that the proposed model outperforms the benchmark model, with mAP50 and mAP75 improving by 4.7% and 5.0%, and 5.3% and 3.3%, respectively, whereas the number of model parameters is only 6.62 M. This study illustrates the utility potential of the algorithm for weed detection in cotton fields, marking a significant advancement of artificial intelligence in agriculture.