基于改进型中心网的轨道表面缺陷检测研究

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yizhou Mao, Shubin Zheng, Liming Li, Renjie Shi, Xiaoxue An
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引用次数: 0

摘要

铁路表面缺陷检测对铁路安全至关重要。传统方法在缺陷大小不一、背景复杂的情况下难以奏效,而两阶段深度学习模型虽然准确,但缺乏实时性。为了克服这些挑战,我们提出了一种基于 CenterNet 的增强型单阶段检测模型。我们用 ResNeXt 代替 ResNet,并实现了多分支结构,以更好地提取底层特征。此外,我们将 SKNet 注意机制与 YOLOv8 的 C2f 结构相结合,提高了模型对关键图像区域的关注度,并增强了对细微缺陷的检测能力。我们还为尺寸回归损失引入了椭圆高斯核,以更好地表示轨道缺陷的长宽比。这种方法提高了检测精度,加快了训练速度。我们的模型在铁路缺陷数据集上达到了 0.952 的平均准确率 (mAP),优于其他模型,比原始模型提高了 6.6%,训练速度提高了 35.5%。这些结果证明了我们的方法在铁路缺陷检测方面的效率和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Rail Surface Defect Detection Based on Improved CenterNet
Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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