Academic Journal of Computing & Information Science, 2024, 7(10); doi: 10.25236/AJCIS.2024.071007.
Zhen Qi1, Liwei Ding2, Xiangtian Li3, Jiacheng Hu4, Bin Lyu5, Ao Xiang6
1Northeastern University, Electrical and Computer Engineering, Boston, USA
2Florida International University, Computer Science, Miami, USA
3University of California San Diego, Electrical and Computer Engineering, San Diego, USA
4Tulane University, Business Analytics, New Orleans, USA
5Kanazawa University, Division of Mathematical and Physical Sciences, Ishikawa, Japan
6Northern Arizona University, Information Security and Assurance, Arizona, USA
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.
Product defect detection, YOLO, deep learning, real-time detection, industrial automation
Zhen Qi, Liwei Ding, Xiangtian Li, Jiacheng Hu, Bin Lyu, Ao Xiang. Detecting and Classifying Defective Products in Images Using YOLO. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 10: 47-54. https://doi.org/10.25236/AJCIS.2024.071007.
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