A Lightweight Method for Ripeness Detection and Counting of Chinese Flowering Cabbage in the Natural Environment

Abstract

The rapid and accurate detection of Chinese flowering cabbage ripeness and the counting of Chinese flowering cabbage are fundamental for timely harvesting, yield prediction, and field management. The complexity of the existing model structures somewhat hinders the application of recognition models in harvesting machines. Therefore, this paper proposes the lightweight Cabbage-YOLO model. First, the YOLOv8-n feature pyramid structure is adjusted to effectively utilize the target’s spatial structure information as well as compress the model in size. Second, the RVB-EMA module is introduced as a necking optimization mechanism to mitigate the interference of shallow noise in the high-resolution sounding layer and at the same time to reduce the number of parameters in this model. In addition, the head uses an independently designed lightweight PCDetect detection head, which enhances the computational efficiency of the model. Subsequently, the neck utilizes a lightweight DySample upsampling operator to capture and preserve underlying semantic information. Finally, the attention mechanism SimAm is inserted before SPPF for an enhanced ability to capture foreground features. The improved Cabbage-YOLO is integrated with the Byte Tracker to track and count Chinese flowering cabbage in video sequences. The average detection accuracy of Cabbage-YOLO can reach 86.4%. Compared with the original model YOLOv8-n, its FLOPs, the its number of parameters, and the size of its weights are decreased by about 35.9%, 47.2%, and 45.2%, respectively, and its average detection precision is improved by 1.9% with an FPS of 107.8. In addition, the integrated Cabbage-YOLO with the Byte Tracker can also effectively track and count the detected objects. The Cabbage-YOLO model boasts higher accuracy, smaller size, and a clear advantage in lightweight deployment. Overall, the improved lightweight model can provide effective technical support for promoting intelligent management and harvesting decisions of Chinese flowering cabbage.