Surface Defect Detection Using YOLO Network

Date

2020-09

Advisors

Journal Title

Journal ISSN

ISSN

2194-5357

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

Detecting defects on surfaces such as steel can be a challenging task because defects have complex and unique features. These defects happen in many production lines and differ between each one of these production lines. In order to detect these defects, the You Only Look Once (YOLO) detector which uses a Convolutional Neural Network (CNN), is used and received only minor modifications. YOLO is trained and tested on a dataset containing six kinds of defects to achieve accurate detection and classification. The network can also obtain the coordinates of the detected bounding boxes, giving the size and location of the detected defects. Since manual defect detection is expensive, labor-intensive and inefficient, this paper contributes to the sophistication and improvement of manufacturing processes. This system can be installed on chipsets and deployed to a factory line to greatly improve quality control and be part of smart internet of things (IoT) based factories in the future. YOLO achieves a respectable 70.66% mean average precision (mAP) despite the small dataset and minor modifications to the network.

Description

Keywords

YOLO (You Only Look Once), Defect Detection, CNN, Computer Vision, Transfer Learning.

Citation

Hatab, M., Malekmohamadi, H., Amira, A. (2020) Surface Defect Detection Using YOLO Network. Intelligent Systems Conference (IntelliSys 2020), Amsterdam, September 2020.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)