|
|
基于改进FCOS的钢带表面缺陷检测算法
|
Abstract:
针对现有钢带表面缺陷检测所存在的检测效率低、适用范围有限等缺陷，提出一种基于改进FCOS的钢带表面缺陷检测算法。该算法使用含形变卷积的卷积神经网络提取缺陷特征，使用关键点特征融合增强检测模型输入，并使用中心采样策略选取训练样本优化模型训练，最后使用东北大学钢带表面缺陷公共数据集进行训练和评估。本文算法在东北大学钢带表面缺陷公共数据集上平均检测精度为74%，检测速度为31.4 FPS。
Aiming at the defects of low detection efficiency and limited applicable scope in strip surface defect detection, a steel strip surface defect detection algorithm based on improved FCOS was proposed. The algorithm uses convolutional neural network with deformable convolution to extract defect features, uses key point feature fusion to enhance the detection model input and uses the central point sampling strategy to select training samples to optimize model training. Finally, the proposed algorithm is trained and evaluated on public dataset NEU-DET, Northeastern University surface defect database. On NEU-DET dataset, the mean average precision of this algorithm achieves 74% and the detection velocity is 31.4 FPS.
| [1] | 吴平川, 路同浚. 钢板表面缺陷的无损检测技术与应用[J]. 无损检测, 2000, 22(7): 312-315. |
| [2] | 顾佳晨, 高雷, 刘路硌. 基于深度学习的目标检测算法在冷轧表面缺陷检测中的应用[J]. 冶金自动化, 2019, 43(6): 19-22. |
| [3] | 王海云, 王剑平, 罗付华. 融合多层次特征Faster R-CNN的金属板带材表面缺陷检测研究[J]. 机械科学与技术, 2021, 40(2): 262-269. |
| [4] | Dai, X., Chen, H. and Zhu, C. (2020) Research on Surface Defect Detection and Implementation of Metal Workpiece Based on Improved Faster R-CNN. Surface Technology, 49, 362-371. |
| [5] | 李维刚, 叶欣, 赵云涛, 等. 基于改进YOLOv3算法的带钢表面缺陷检测[J]. 电子学报, 2020, 48(7): 1284-1292. |
| [6] | 刘艳菊, 王秋霁, 赵开峰, 等. 基于卷积神经网络的热轧钢条表面实时缺陷检测[J/OL]. 仪器仪表学报: 1-10.
http://kns.cnki.net/kcms/detail/11.2179.TH.20211230.1604.024.html |
| [7] | Tian, Z., Shen, C., Chen, H., et al. (2019) FCOS: Fully Convolutional One-Stage Object Detection. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 27 October-2 November 2019, 9627-9636.
https://doi.org/10.1109/ICCV.2019.00972 |
| [8] | Dai, J., Qi, H., Xiong, Y., et al. (2017) Deformable Convolutional Networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 764-773. https://doi.org/10.1109/ICCV.2017.89 |
| [9] | Song, K. and Yan, Y. (2013) A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects. Applied Surface Science, 285, 858-864. https://doi.org/10.1016/j.apsusc.2013.09.002 |
| [10] | Lin, T.-Y., Maire, M., Belongie, S., et al. (2014) Microsoft COCO: Common Objects in Context. European Conference on Computer Vision, Zurich, 6-12 September 2014, 740-755. https://doi.org/10.1007/978-3-319-10602-1_48 |
