Improved Feature Fusion in YOLOv5 for Accurate Detection and Counting of Chinese Flowering Cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) Buds

Abstract

Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial for production. The open state of Chinese flowering cabbage buds serves as a crucial maturity indicator. To address the challenge of accurately identifying Chinese flowering cabbage buds, we introduced improvements to the feature fusion approach of the YOLOv5 (You Only Look Once version 5) algorithm, resulting in an innovative algorithm with a dynamically adjustable detection head, named FPNDyH-YOLOv5 (Feature Pyramid Network with Dynamic Head-You Only Look Once version 5). Firstly, a P2 detection layer was added to enhance the model’s detection ability of small objects. Secondly, the spatial-aware attention mechanism from DyHead (Dynamic Head) for feature fusion was added, enabling the adaptive fusion of semantic information across different scales. Furthermore, a center-region counting method based on the Bytetrack object tracking algorithm was devised for real-time quantification of various categories. The experimental results demonstrate that the improved model achieved a mean average precision (mAP@0.5) of 93.9%, representing a 2.5% improvement compared to the baseline model. The average precision (AP) for buds at different maturity levels was 96.1%, 86.9%, and 98.7%, respectively. When applying the trained model in conjunction with Bytetrack for video detection, the average counting accuracy, relative to manual counting, was 88.5%, with class-specific accuracies of 90.4%, 80.0%, and 95.1%. In conclusion, this method facilitates relatively accurate classification and counting of Chinese flowering cabbage buds in natural environments.