A fast and robust convolutional neural network-based defect detection model in product quality control

作者：Wang, Tian^*; Chen, Yang; Qiao, Meina; Snoussi, Hichem

来源：International Journal of Advanced Manufacturing Technology, 2018, 94(9-12): 3465-3471.

DOI：10.1007/s00170-017-0882-0

• Product quality control
• Defect detection
• Convolutional neural networks

The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.

• 出版日期2018-2
• 单位北京航空航天大学