Apple surface defect identification based on image analysis Online publication date: Sat, 13-Aug-2022
by Qunpo Liu; Yuxi Zhao; Jianjun Zhang; Ruxin Gao
International Journal of Cybernetics and Cyber-Physical Systems (IJCCPS), Vol. 1, No. 2, 2022
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Cybernetics and Cyber-Physical Systems (IJCCPS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook