Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects

Seong-Hwan Kang, Vikas Palakonda, Il-Min Kim, Jae-Mo Kang () and Sangseok Yun ()
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Seong-Hwan Kang: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Vikas Palakonda: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Il-Min Kim: Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
Jae-Mo Kang: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Sangseok Yun: Department of Information and Communications Engineering, Pukyong National University, Busan 48513, Republic of Korea

Mathematics, 2023, vol. 11, issue 18, 1-14

Abstract: Quality control in manufacturing equipment relies heavily on the detection of steel surface defects. Recently, there have been an increasing number of efforts in which object detection techniques have been utilized to achieve promising results in the detection of steel surface defects since the defect patterns can be considered objects. To enhance the detection performance in the object detection problem, the non-maximum suppression (NMS) step, which eliminates redundant boxes overlapped with a box having the greatest detection score, is essential. In this work, we propose a novel NMS to improve the detection method of steel surface defects. The proposed NMS approach is composed of three novel techniques: IoU regularization, threshold adjustment, and comparison rule modification to enhance the detection performance. To evaluate the performance of the proposed NMS, we carry out extensive numerical experiments using the YOLOv7 and EfficientDet models on the steel surface defect datasets, NEU-DET and GC10-DET. The experimental results demonstrate that the proposed NMS outperforms the conventional NMS methods in both quantitative and qualitative manners.

Keywords: computer vision; deep learning; non-maximum suppression; object detection; steel surface defect (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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