Improved YOLOv5s recognition of cotton top buds with fusion of attention and feature weighting

Abstract

In order to improve the accuracy and real-time performance of cotton top bud recognition, an improved YOLOv5s target real-time detection model is proposed. First, the SE module and the CBAM module in the attention mechanism are added to optimize the weight ratio of channel attention and spatial attention to improve accuracy; then the BiFPN structure of bidirectional weighted features is introduced to strengthen the fusion between high-level features and low-level features; finally, a new bounding box regression loss function EIoU is used for ablation experiments, and more position information of cotton buds can be obtained by reducing the bounding box loss. The experimental results show that, by applying the improved algorithm in the identification of cotton top buds, compared with the original YOLOv5s model, the accuracy of the C3SE-4l+BiFPN+EIoU model has increased by 7.9%, the recall rate has increased by 2.8%, and the average precision an increase of 5.7%. These improvements and optimizations provide a new idea and method, which can provide a more efficient solution for the identification of cotton top buds.