Research on the Detection Method of Safflower Filaments in Natural Environment Based on Improved YOLOv5s

摘要

The accurate and rapid identification of safflower filaments is a prerequisite for automating harvesting. This paper proposes a lightweight, high-precision detection model for safflower filaments based on YOLOv5s, named YOLOv5s-MCD, to address the issues of large existing network model sizes and low detection accuracy in complex natural environments. The Backbone of the YOLOv5s-MCD model was optimized into a lightweight improved network MobileNetv2 with DSC and CA modules, and the neck part incorporated the CA attention mechanism. The loss function is improved from DIoU’s non-maximum suppression method to CIoU to reduce the model size and improve the detection accuracy and speed. The experimental results show that the size of the YOLOv5s-MCD model is its size by 7.69 MB compared to the original YOLOv5s model, with a mean Average Precision (mAP) of 95.6% and an average detection time of only 3.2ms per image. When tested under unobstructed, obstructed, backlighting, shaking, and wide-angle natural environments, the improved YOLOv5s-MCD model increased the mAP value by 4.4, 0.7, 3.3, 3.4, and 1.0 percentage, respectively, compared with the YOLOv5s model, with improved F1 scores and confidence levels. This indicates that the improved lightweight model can achieve fast, real-time, and accurate detection of the safflower filaments in complex environments. The research results can provide a technical reference for the development of field safflower filament-harvesting robots.