Tiny defect detection algorithm for bearing surface based on RT-DETR

doi:10.11772/j.issn.1001-9081.2024050691

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (6): 1987-1997.DOI: 10.11772/j.issn.1001-9081.2024050691

• Multimedia computing and computer simulation • Previous Articles

Tiny defect detection algorithm for bearing surface based on RT-DETR

Dehui ZHOU¹, Jun ZHAO¹(), Jinfeng CHENG²

^1.School of Mechatronic Engineering，Lanzhou Jiaotong University，Lanzhou Gansu 730070，China
^2.Lanzhou Ruierwen Rail Transit Technology Company Limited，Lanzhou Gansu 730070，China

Received:2024-05-28 Revised:2024-08-15 Accepted:2024-08-20 Online:2024-09-04 Published:2025-06-10
Contact: Jun ZHAO
About author:ZHOU Dehui， born in 1999， M. S. candidate. His research interests include computer vision， deep learning.
ZHAO Jun， born in 1975， Ph. D.， professor. His research interests include machine vision， object detection.
CHENG Jinfeng， born in 1980， M. S.， senior engineer. His research interests include defect detection， mechanical non-destructive testing.
Supported by:
National Natural Science Foundation of China(51868037)

基于RT-DETR的轴承表面微小缺陷检测算法

周得辉¹, 赵军¹(), 程进峰²

^1.兰州交通大学机电工程学院，兰州 730070
^2.兰州瑞尔文轨道交通科技有限公司，兰州 730070

通讯作者: 赵军
作者简介:周得辉（1999 —），男，甘肃兰州人，硕士研究生，CCF会员，主要研究方向：计算机视觉、深度学习
赵军（1975—），男，甘肃古浪人，教授，博士，主要研究方向：机器视觉、目标检测 zhaojun@mail.lzjtu.cn
程进峰（1980—），男，甘肃兰州人，高级工程师，硕士，主要研究方向：缺陷检测、机械无损探伤。
基金资助:
国家自然科学基金资助项目(51868037)

Abstract

Abstract:

Surface defects of bearing have a significant impact on performance and stability of electromechanical equipment. Aiming at the issues of low recognition accuracy of small targets and low detection speed in current surface defect detection process for bearings， a tiny defect detection algorithm of bearing surface based on RT-DETR （Real-Time DEtection TRansformer） — FECS-DETR （Faster Expand and Cross hierarchical-scaled feature Screening DETR） algorithm was proposed. Firstly， a lightweight FasterNet-T1 was employed to reconstruct the backbone network of RT-DETR for reducing computational overhead. Secondly， an Attention-embedded Expand Residual Fusion （AERF） module was designed for deep feature extraction， thereby enhancing the description capability of small-scale abstract features. Thirdly， a Cascaded Group Attention （CGA） was applied to further reduce computational redundancy and improve operational efficiency of the model. Fourthly， a Cross hierarchical-scaled Information Screening Feature Pyramid Network （CIS-FPN） was proposed to address the issue of information loss during feature fusion and enhance feature integration capability. Finally， a joint regression loss optimization strategy combining Normalized Wasserstein Distance （NWD） and improved Inner-MPDIoU was employed to accelerate model’s convergence and improve model accuracy for small-scale targets. Experimental results show that on the bearing surface tiny defect dataset， compared with the original RT-DETR algorithm， FECS-DETR algorithm has the mean Average Precision （mAP） improved by 2.5 percentage points， the computation complexity reduced by 28.8%， and the detection speed increased by 20.8%. It can be seen that the proposed algorithm achieves a balance between accuracy and real-time performance， and satisfies the requirements for detection of bearing surface tiny defects in industrial environment.

Key words: bearing surface defect detection, small target, RT-DETR (Real-Time DEtection TRansformer), feature pyramid network, loss function

摘要：

轴承表面缺陷对机电设备的性能和稳定性有显著影响。针对当前轴承表面缺陷检测过程中存在的小目标识别精度不高、速度较慢的问题，提出一种基于RT-DETR（Real-Time DEtection TRansformer）的轴承表面微小缺陷检测算法——FECS-DETR（Faster Expand and Cross hierarchical-scaled feature Screening DETR）算法。首先，采用轻量级FasterNet-T1重构RT-DETR主干网络以降低计算开销；其次，设计内嵌注意力的扩张残差融合（AERF）模块用于提取深层特征，从而增强对小尺度抽象特征的描述能力；再次，通过引入级联分组注意力（CGA），进一步降低计算冗余，并提升模型的运行效率；继次，提出一种跨层级尺度的信息筛选特征金字塔网络（CIS-FPN），以解决特征融合过程中的信息丢失问题，并增强特征融合能力；最后，利用归一化Wasserstein距离（NWD）与改进Inner-MPDIoU联合的回归损失优化策略提高模型收敛速度和模型检测小尺度目标的准确性。实验结果表明，相较于原RT-DETR算法，FECS-DETR算法在轴承表面微小缺陷数据集上的平均精度均值（mAP）提升了2.5个百分点，计算量减少了28.8%，帧率提升了20.8%。可见，所提算法实现了准确率与实时性之间的平衡，能够满足工业环境下的轴承表面微小缺陷检测需求。

关键词: 轴承表面缺陷检测, 小目标, RT-DETR, 特征金字塔网络, 损失函数

CLC Number:

TP391.41

Dehui ZHOU, Jun ZHAO, Jinfeng CHENG. Tiny defect detection algorithm for bearing surface based on RT-DETR[J]. Journal of Computer Applications, 2025, 45(6): 1987-1997.

周得辉, 赵军, 程进峰. 基于RT-DETR的轴承表面微小缺陷检测算法[J]. 《计算机应用》唯一官方网站, 2025, 45(6): 1987-1997.

Figures/Tables 20

References 39

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网络	计算量/GFLOPs	参数量/10⁶
ResNet18	1.80	11.7
ResNet34	3.60	21.8
FasterNet-T1	0.85	7.6
FasterNet-T2	1.90	15.0

网络	计算量/GFLOPs	参数量/10⁶
ResNet18	1.80	11.7
ResNet34	3.60	21.8
FasterNet-T1	0.85	7.6
FasterNet-T2	1.90	15.0

方法	P/%	R/%	mAP/%	计算量/GFLOPs	帧率/ （frame·s^-1）
None	89.5	84.7	88.4	37.2	125
CPCA	90.5	84.0	88.7	48.2	108
SGE	90.4	86.0	89.5	47.7	112
CA	90.4	86.2	89.8	47.7	112
CBAM	91.2	86.0	90.0	47.7	112
EffectiveSE	91.5	85.9	90.3	47.7	112

方法	P/%	R/%	mAP/%	计算量/GFLOPs	帧率/ （frame·s^-1）
None	89.5	84.7	88.4	37.2	125
CPCA	90.5	84.0	88.7	48.2	108
SGE	90.4	86.0	89.5	47.7	112
CA	90.4	86.2	89.8	47.7	112
CBAM	91.2	86.0	90.0	47.7	112
EffectiveSE	91.5	85.9	90.3	47.7	112

实验	FasterNet-T1	AERF	CGA+CIS-FPN	NWD+Inner-MPDIoU	P/%	R/%	mAP/%	计算量/ GFLOPs	帧率/ （frame·s^-1）
Ⅰ					90.3	83.8	88.2	57.0	96
Ⅱ	√				89.5	84.7	88.4	37.2	125
Ⅲ		√			91.1	85.6	90.2	60.7	90
Ⅳ			√		90.8	86.1	90.2	49.9	98
Ⅴ				√	91.2	83.9	88.6	57.0	96
Ⅵ	√	√			91.5	85.9	90.3	47.7	112
Ⅶ	√	√	√		91.6	86.1	90.6	40.6	116
Ⅷ	√	√	√	√	92.0	86.5	90.7	40.6	116

Tiny defect detection algorithm for bearing surface based on RT-DETR

基于RT-DETR的轴承表面微小缺陷检测算法

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Figures/Tables 20

References 39

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Metrics

算法	P/%	R/%	mAP/%	计算量/ GFLOPs	帧率/ （frame·s^-1）
Faster R-CNN^［31］	87.9	80.8	85.4	178.5	16
文献［9］算法	89.2	81.9	86.6	75.4	67
文献［32］算法	88.2	81.3	86.9	19.4	116
文献［33］算法	90.0	83.6	87.8	110.4	132
YOLOv8m^［34］	90.2	83.4	88.1	78.7	95
RT-DETR-r18^［14］	90.3	83.8	88.2	57.0	96
YOLOv10m^［35］	88.7	83.4	88.4	58.9	94
RT-DETR-KAN^［36］	90.6	86.8	90.0	117.4	40
RT-DETR-Mamba^［37］	90.9	87.2	90.4	54.5	72
FECS-DETR	92.0	86.5	90.7	40.6	116

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