Detecting and Classifying Defective Products in Images Using YOLO

TL;DR

This paper demonstrates how the YOLO deep learning algorithm can be effectively used for real-time detection and classification of product defects in industrial images, improving inspection efficiency and accuracy.

Contribution

It introduces a YOLO-based approach for defect detection in industrial products, highlighting its practical advantages and limitations.

Findings

Achieves high detection accuracy in real-time

Significantly improves inspection efficiency

Analyzes YOLO's practical application limitations

Abstract

With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent years, deep learning technology, especially the YOLO (You Only Look Once) algorithm, has emerged as a prominent solution in the field of product defect detection due to its efficient real-time detection capabilities and excellent classification performance. This study aims to use the YOLO algorithm to detect and classify defects in product images. By constructing and training a YOLO model, we conducted experiments on multiple industrial product datasets. The results demonstrate that this method can achieve real-time detection while maintaining high detection accuracy, significantly improving the efficiency and accuracy of product quality inspection. This paper further analyzes the advantages and limitations of the YOLO algorithm in practical applications and explores future research directions.