RECOGNITION METHOD FOR SEED POTATO BUDS BASED ON IMPROVED YOLOV3-TINY
This paper proposed a method of seed potato buds recognition based on improved YOLOv3-tiny. K-means clustering based on IoU is used to obtain the anchor box that meets the size of buds. Mosaic online data enhancement is used to increase image diversity and model generalization ability. The CIoU bounding box regression loss function is introduced to improve the regression effect of buds recognition. The results show that the precision (P), the recall (R), the average precision (AP), and the F1 score of the model for seed potato buds recognition are 88.33%, 85.97%, 91.18% and 87.13% respectively. The real-time recognition speed of seed potato buds on the embedded platform NVIDIA Jetson Nano can reach 40FPS. The method proposed in this paper can meet the needs of real-time recognition of seed potato buds on the embedded platform.
Abstract in Chinese