计算机工程与应用2025，Vol.61Issue(21)：117-128,12.DOI:10.3778/j.issn.1002-8331.2411-0110
改进YOLOv8的列车转向架螺栓检测方法研究
Research on Improved YOLOv8 Based Bolt Detection Method for Train Bogies
摘要
Abstract
A multi-feature fusion enhancement algorithm based on YOLOv8 is proposed to address the challenges of detecting small bolts in the complex environment and low-resolution conditions at the bottom of a train's undercarriage.Adaptive contrast stretching is applied during image preprocessing to enhance image quality and highlight bolt details,providing high-quality input for the detection algorithm.The SPD-Conv module is introduced to replace traditional stride convolutions and pooling operations,minimizing fine-grained information loss in small object detection.a BoSTNet archi-tecture is designed to optimize the backbone network and retain small bolt target information effectively.In the Neck layer,a parallel dynamic weighted multi-dimensional fusion attention module is integrated to further suppress noise.In order to accelerate model convergence and improve regression accuracy,the Focaler-MPDIoU function is introduced to optimize the bounding box regression loss so as to efficiently locate the bolt loss function comparison experiments.Exper-imental results show that,on a custom dataset,the improved YOLOv8 achieves a 3.9,3.2,and 4.8 percentage points increase in detection accuracy,recall rate,and mAP50,respectively,with values of 95.1%,94.6%,and 95.0%.This dem-onstrates the model's high efficiency in detecting small bolts under complex conditions.Moreover,on the VisDrone-2019 dataset,the improved YOLOv8 outperforms other detection methods,further validating its applicability in complex scenes and small object detection.关键词
螺栓检测/YOLOv8算法/卷积神经网络/混合注意力机制Key words
bolt detection/YOLOv8 algorithm/convolutional neural network/hybrid attention mechanism分类
信息技术与安全科学引用本文复制引用
胡贺南,何秋禹,李荣华,王大志,张然..改进YOLOv8的列车转向架螺栓检测方法研究[J].计算机工程与应用,2025,61(21):117-128,12.基金项目
国家自然科学基金(20191258) （20191258）
国家"863"计划项目(2007AA11Z180) （2007AA11Z180）
辽宁省教育厅科学研究项目(LJ212410150036). （LJ212410150036）
