An Object Detection Model for Paint Surface Detection Based on Improved YOLOv3

Abstract

To solve the problem of poor performance of the target detection algorithm and false detection in the detection of paint surface defects of office chairs five-star feet, we propose a defect detection method based on the improved YOLOv3 algorithm. Firstly, a new feature fusion structure is designed to reduce the missed detection rate of small targets. Then we used the CIOU loss function to improve the positioning accuracy. At the same time, a parallel version of the k-means++ initialization algorithm (K-means||) is used to optimize and determine the parameters of the a priori anchor so as to improve the matching degree between the a priori anchor and the feature layer. We constructed a dataset of paint surface defects on the five-star feet of office chairs and performed optimization training, and used multiple algorithms and different datasets to conduct comparative experiments to validate the algorithm. The experimental results show that the improved YOLOv3 algorithm is effective in that the average precision on the self-made dataset reaches 88.3%, which is 5.8% higher than the original algorithm. At the same time, it has also been verified based on the Aliyun Tianchi competition aluminum dataset, and the average precision has reached 89.2%. This method realizes the real-time detection of the paint surface defects of the five-star feet of the office chair very well.