Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN.

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ID: 71424
2019
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Abstract
The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.
Reference Key
zhou2019detectionsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhou, Zhuangzhuang;Lu, Qinghua;Wang, Zhifeng;Huang, Haojie;
Journal sensors
Year 2019
DOI
E5000
URL
Keywords
Defect detection k-means feature fusion irregular surfaces

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