Surface Defect Detection Algorithm of Micro-Channel Aluminum Flat Tube Based on Improved FCOS Model

doi:10.3778/j.issn.1002-8331.2208-0204

Abstract

Abstract: Aiming at the task of detecting surface defects of micro-channel aluminum flat tubes, an improved FCOS（fully convolutional one-stage object detection） algorithm is proposed. Firstly, a feature convolution pyramid network is designed to enable the model to adaptively mix feature maps from different layers for detection. Secondly, through analyzing the limitations of the original FCOS algorithm in the detection of long and narrow defects, and the positive sample deployment strategy of the model is improved, the missed detection of long and narrow defects is reduced. Then, a more suitable mapping function and center-ness function are designed to solve the regression problem and center-ness calculation problem of positive sample points outside the labeled frame. Finally, the EIoU（efficient IoU） loss is used to replace the IoU loss in the original model to further improve the regression ability of the model. The experimental results show that in the surface defect detection task of micro-channel aluminum flat tubes, the improved FCOS model achieves the mAP（mean average precision） of 76.4%, which is 7.7 percentage points higher than the original model.

Key words: micro-channel aluminum flat tube, detection of long and narrow defects, sample deployment, feature convolutional pyramid, fully convolutional one-stage object detection（FCOS） model

摘要： 针对微通道铝扁管表面缺陷的检测问题，提出了一种改进的FCOS（fully convolutional one-stage object detection）算法。设计一种特征卷积金字塔网络（feature convolutional pyramid network，FCPN），使模型能够自适应地混合不同层的特征图进行检测；在分析原始FCOS应用于狭长缺陷检测局限性的基础上，改进了模型的正样本部署策略，以降低模型对狭长缺陷的漏检；设计更加合适的映射函数与中心度函数，解决标注框外正样本点的回归问题与中心度计算问题；使用EIoU损失（efficient IoU loss）替换原模型中的IoU损失，进一步提高模型的回归能力。实验结果表明，在微通道铝扁管的表面缺陷检测任务中，改进后的FCOS模型达到了76.4%的mAP（mean average precision），相比于原始模型提高了7.7个百分点。

关键词: 微通道铝扁管, 狭长缺陷检测, 样本部署, 特征卷积金字塔, FCOS模型

GUI Penghui, SONG Tao, TANG Jianbin, XU Zhipeng, CAO Songxiao, JIANG Qing. Surface Defect Detection Algorithm of Micro-Channel Aluminum Flat Tube Based on Improved FCOS Model[J]. Computer Engineering and Applications, 2023, 59(24): 298-308.

桂鹏辉, 宋涛, 汤建斌, 徐志鹏, 曹松晓, 蒋庆. 改进FCOS模型的微通道铝扁管表面缺陷检测算法[J]. 计算机工程与应用, 2023, 59(24): 298-308.

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