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Surface Defect Detection Algorithm Based on Feature-Enhanced YOLO

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Abstract

Surface defect detection is a complicated task to achieve both specific class and precise location of each defect. Specifically for industrial scenario, realizing efficient and accuracy-satisfactory surface defect automatic detection is still a big challenge. Therefore, a surface defect detection algorithm based on feature-enhanced YOLO (FE-YOLO) for practical industrial applications is proposed in this paper. For the purpose of efficient detection, we lighten YOLO model by combining deep separable convolution and dense join. And an improved feature pyramid network is proposed to enhance the spatial location correlation for multi-scale detection layer for the sake of high accuracy. Then, a new loss function of prediction box regression is established to boost the detection accuracy under the high Intersection over Union (IoU) threshold and accelerate model convergence. To select anchor boxes of different scale feature detection layers, we propose a statistical-based k-means++ algorithm, which can improve the quality of initial anchors and accelerate the convergence of the proposed model. Two industrial surface defect datasets, NEU-DET dataset and DeepPCB dataset, are used to verify the effectiveness of the proposed FE-YOLO algorithm. Experimental results demonstrate that FE-YOLO algorithm is lightened nearly 80% compared with YOLOV4. The detection speed is better than the other state-of-the-art surface defect detection algorithms. The defects detection accuracy respectively reaches 83.9% and 98.9% for the NEU-DET dataset and DeepPCB dataset, which are better than the state-of-the-art defect detection methods. The end-to-end fast and accurate detection for industrial surface defects is realized.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded in part by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2021B0101200005), the State Key Program of National Natural Science of China (Grant No. 62233018), the National Natural Science Foundation of China (Grant No. 62003370), and the Nature Science Foundation of Hunan province (Grant No. 2021JJ30873).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Yongfang Xie, Weitao Hu, Shiwen Xie & Lei He

  2. Engineering Research Center of Nonferrous Metallurgical Automation, Ministry of Education, Central South University, Changsha, 410083, China

    Yongfang Xie, Weitao Hu, Shiwen Xie & Lei He

Authors
  1. Yongfang Xie
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  2. Weitao Hu
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  3. Shiwen Xie
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  4. Lei He
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Correspondence to Shiwen Xie.

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Xie, Y., Hu, W., Xie, S. et al. Surface Defect Detection Algorithm Based on Feature-Enhanced YOLO. Cogn Comput 15, 565–579 (2023). https://doi.org/10.1007/s12559-022-10061-z

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