Abstract
The apple fruit defect detection is a necessary step before apples enter the market. When using deep learning to detect apple defects, apple defects are prone to miss detection and inaccurate positioning due to multiple convolutions and down-sampling. Therefore, this paper proposes YOLO-APPLE model. Three residual blocks in YOLOV3 were replaced with three dense blocks, and feature transfer between dense connected blocks was strengthened by combining average pooling to improve feature reuse, so as to reduce the rate of missed detection. Complete-IOU is used as the regression loss to locate the prediction frame more accurately. Secondly, K-means clustering algorithm was used for clustering apple defect dataset to obtain anchor boxes more consistent with apple defect and raise the efficiency of precision of the model. The results showed that the average precision of YOLO-APPLE model is 93.53%, and the detection speed is 43FPS, which can detect in real time.
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