Detection Method of Damaged Camellia Oleifera Seeds Based on YOLOv5-CB

Abstract

To solve the problems that the existing sorting equipment cannot effectively identify and sort damaged Camellia oleifera seeds and traditional manual sorting of damaged Camellia oleifera seeds is inefficient and slow, in this paper, a damaged Camellia oleifera seeds detection method based on YOLOv5, coordinate attention, and weighted bidirectional feature pyramid network was designed. In this study, according to the actual requirements, firstly, the Coordinate Attention module (CA) was added to the YOLOv5 algorithm to improve the detection precision of damaged Camellia oleifera seeds in stacked Camellia oleifera seeds. Secondly, the network structure was optimized and the weighted bi-directional feature pyramid network (BiFPN) was added. The module integrates multi-scale features from top to bottom to reduce the missed detection of slightly damaged Camellia seeds. The final experimental results show that compared with the original YOLOv5 model, the detection precision of the improved model YOLOV5-CB is improved by 6.1%, reaching 92.4%, and the mean Average Precision (mAP) is also improved from 87.7% to 93.4%, the average detection time of a single Camellia seeds image is 6.4ms, which meet the requirements of precision and real-time in practical application.