A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies

Sensors

Gaokai Liu¹  Zhi Chen¹  Shiping Guo¹  Lei Guo¹  Ning Yang¹ 

[1] School of Automation, Northwestern Polytechnical University, Xi’an 710129, China;

关键词: surface anomaly detection;    computer vision;    deep learning;    one stage;    background suppression;   

DOI  :  10.3390/s20071829

来源: DOAJ

【 摘 要 】

We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and fine manners. On the other hand, a classification module without learning parameters is built to reduce information loss in small targets due to the inexistence of successive down-sampling processes. Experimental results demonstrate that our one-stage detector achieves state-of-the-art performance in terms of precision, recall and f-score.

【 授权许可】

Unknown