基于FCOS神经网络的制动主缸内槽缺陷检测方法

doi:10.3969/j.issn.1000-1158.2021.09.17

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摘要针对主缸内槽缺陷检测存在干扰因素复杂、检测精度低等难点,提出了一种基于全卷积单阶段神经网络(FCOS)的主缸内槽缺陷检测算法。利用特征融合金字塔网络进行特征提取并逐像素预测,得到缺陷种类,实现凹槽缺陷的自动检测。实验结果表明,FCOS网络对制动主缸内槽砂眼、划痕、振刀纹缺陷检测的平均精度均值分别为85.2％、87.5％、90.1％,精确度分别为0.98、0.89、0.95。实验结果与Mask R-CNN网络和Faster R-CNN网络的实验结果进行对比,FCOS网络具有更高的准确率,学习时长大幅度缩短,且满足实时检测要求。

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	王芷薇
	郭斌
	胡晓峰
	罗哉
	段林茂

关键词 ：计量学, 内槽缺陷检测, 制动主缸, 全卷积网络, FCOS, 特征融合金字塔网络

Abstract：Aiming at the difficulties of complicated interference factors and low detection accuracy in the detection of groove defects in the main cylinder, a detection algorithm for groove defects in the main cylinder based on full convolution single stage neural network (FCOS) was proposed. FPN network was used for feature extraction and pixel by pixel prediction, and the predicted results were classified to realize automatic detection of groove defects. The experimental results show that the mAP values of FCOS network in detecting the sand hole, scratch and vibration pattern in the inner groove of the main cylinder are 85.2%, 87.5% and 90.1%,and the detection accuracy is 0.98, 0.89 and 0.95. Finally, the experimental results were compared with those of the Mask R-CNN network and Faster R-CNN network. FCOS network had higher accuracy, significantly shortened learning time and satisfied real-time detection requirements.

Key words： metrology detection of groove defects brake master cylinder full convolutional network FCOS feature pyramid network

收稿日期: 2020-04-26 发布日期: 2021-09-24

PACS:

TB973

基金资助:国家自然科学基金(51675499);国家自然科学基金重大科研仪器研制项目(51927811);浙江省先进制造技术重点实验室开发项目(2019KF01)

通讯作者: 胡晓峰(1987-),男,浙江台州人,中国计量大学实验师,从事汽车零部件智能检测技术研究及精密仪器研制。Email: 12b0202094@cjlu.edu.cn E-mail: 12b0202094@cjlu.edu.cn

作者简介: 王芷薇(1997-),女,吉林省吉林市人,中国计量大学研究生,主要研究方向为机器学习、深度学习理论及应用。Email: p1802085257@cjlu.edu.cn

引用本文:

王芷薇,郭斌,胡晓峰,罗哉,段林茂. 基于FCOS神经网络的制动主缸内槽缺陷检测方法[J]. 计量学报, 2021, 42(9): 1225-1231.
WANG Zhi-wei,GUO Bin,HU Xiao-feng,LUO Zai,DUAN Lin-mao. Internal Groove Defect Detection Method of Brake Master Cylinder Based on FCOS Neural Network. Acta Metrologica Sinica, 2021, 42(9): 1225-1231.

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