探测器
曲面(拓扑)
功能(生物学)
特征(语言学)
人工智能
领域(数学)
模式识别(心理学)
计算机科学
几何学
数学
电信
语言学
进化生物学
生物
哲学
纯数学
作者
Rushuai Tian,Minping Jia
出处
期刊:Measurement
[Elsevier]
日期:2022-01-01
卷期号:187: 110211-110211
被引量:95
标识
DOI:10.1016/j.measurement.2021.110211
摘要
In recent years, surface defect detection methods based on deep learning have been widely used. A conflict between speed and accuracy, however, still exists. In this paper, a steel surface defect detector, DCC-CenterNet, is proposed to achieve the best speed-accuracy trade-off. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, a dilated feature enhancement model is proposed to enlarge the receptive field of the detector. Secondly, a new centerness function center-weight is proposed to make the keypoint estimation more accurate. Then, the CIoU loss that considers the overlap area and aspect ratio of the defect is adopted in the size regression. Finally, the results of experiments show that the accuracy of DCC-CenterNet can reach 79.41 mAP, and the running speed FPS is 71.37 with input size 224 × 224 on the NEU-DET steel defect dataset. And it reaches 61.93 mAP on the GC10-DET steel sheet surface defect dataset at a running speed of 31.47 FPS with input size 512 × 512. It demonstrates that the developed detector can detect steel surface defects efficiently and effectively.
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