基于机器视觉的摇枕吊裂纹识别与探伤评价
杨帆1赵梦娇2陈颖1蒋雪3
Crack Identification and Flaw Detection Eva-luation of Bolster Hanger Based on Machine Vision
YANG Fan1ZHAO Mengjiao2CHEN Ying1JIANG Xue3
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作者信息:1.长春中车轨道交通装备有限公司，130062，长春
2.大连交通大学詹天佑学院，116028，大连
3.沈阳大方电气有限公司，110003，沈阳
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Affiliation:1.Changchun CRRC Rail Vehicles Facilities Co., Ltd., 130062, Changchun, China
2.Zhan Tianyou College, Dalian Jiaotong University, 116028, Dalian, China
3.Shenyang Dafang Electric Co., Ltd., 110003, Shenyang, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.02.027
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中图分类号/CLCN:TP391.4；U270.7
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栏目/Col:应用技术
摘要:
［目的］摇枕吊作为摇枕弹簧悬挂装置中的关键组成部分，因动载荷过大、使用年限长等因素，摇枕吊极易产生疲劳裂纹，对铁路的行车安全有重大影响。为满足摇枕吊磁粉探伤智能化的需求，辅助检测人员完成探伤工作，特开展基于机器视觉的摇枕吊裂纹识别与探伤评价研究。［方法］针对YOLOv5算法主干网络在捕捉裂纹信息能力上的局限，提出将SimAM(相似性感知激活模块)机制融入主干网络，旨在增强模型对裂纹信息的敏感度，并同步提升其抵抗背景噪声干扰的能力。另外，针对Neck(颈部)网络部分在特征融合阶段可能遭遇的信息丢失挑战，引入了一种BiFPN(增强的特征金字塔网络)结构，以实现不同尺度特征图的高效融合。通过实施加权融合策略与双向连接机制，有效减少了底层关键信息的损失，进而优化了特征融合的整体效果。［结果及结论］通过搭建的摇枕吊图像采集系统对YOLOv5-SA-BF模型进行性能测试，试验表明改进算法的MAP(均值平均精度)指标达到了98.08%，较原模型提升了2.91%，Recall(召回率)指标提升了4.13%，该模型有效解决了背景误判、检测精度低的问题，满足了实际检测中的需求。
Abstracts:
［Objective］ The bolster hanger, as a critical component of the bolster spring suspension system, is highly susceptible to fatigue cracks due to excessive dynamic loads and prolonged service life, significantly impacting railway operational safety. To meet the intelligent magnetic particle flaw detection requirements for bolster hangers and assist inspection personnel in the flaw detection work, special research on crack identification and flaw detection evaluation of bolster hangers is conducted based on machine vision. ［Method］ To address the limitations of the YOLOv5 algorithm backbone network in capturing crack information, it is proposed to integrate the SimAM (similarity-aware attention module) mechanism into the backbone network to enhance the model′s sensitivity to crack information and its resistance to background noise interference synchronously. In addition, to overcome the potential information loss during the feature fusion stage of the Neck network, an enhanced BiFPN (bidirectional feature pyramid network) structure is introduced for efficient fusion of multi-scale feature maps. By implementing a weighted fusion strategy and bidirectional connection mechanism, the loss of critical bottom-level information is effectively reduced. ［Result & Conclusion］ The performance of the YOLOv5-SA-BF model is tested using an image acquisition system for bolster hangers. Experimental results demonstrate that the improved algorithm achieved a MAP (mean average precision) of 98.08%, representing a 2.91% increase over the original model, and a Recall increase of 4.13%. The model effectively addresses background false positives and low detection accuracy issues, meeting the requirements in actual inspection.
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