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Open AccessArticle

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

School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
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Sensors 2020, 20(7), 1829; https://doi.org/10.3390/s20071829
Received: 27 January 2020 / Revised: 19 March 2020 / Accepted: 20 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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. View Full-Text
Keywords: surface anomaly detection; computer vision; deep learning; one stage; background suppression surface anomaly detection; computer vision; deep learning; one stage; background suppression
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Liu, G.; Yang, N.; Guo, L.; Guo, S.; Chen, Z. A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies. Sensors 2020, 20, 1829.

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