计算机工程2025，Vol.51Issue(6)：349-359,11.DOI:10.19678/j.issn.1000-3428.0068539
基于路侧相机的自适应空间变换车辆检测方法
Adaptive Spatial Transformation Method for Vehicle Detection Based on Roadside Cameras
摘要
Abstract
To address the challenges in vehicle detection accuracy and efficiency using roadside cameras,this study presents an innovative vehicle detection framework that synergizes Convolutional Neural Network(CNN)and the Transformer architecture.Given the intricacies of traffic scenarios,we devise an adaptive spatial Transformer and combine it with ResNet50 to form a robust backbone network capable of managing diverse vehicle orientations and scales.We further refine the Transformer's input using position encodings grounded on angles and distances to ensure optimal spatial information utilization.A channel-space attention mechanism is incorporated to enhance the global contextual understanding of the images.In the decoding phase,the autoregressive approach is eschewed,facilitating parallel decoding of multiple targets,and the target query embeddings are integrated for vehicle detection tasks.Empirical evaluations on the UA-DETRAC,IITM-hetra and a proprietary dataset yield mAP@0.5 scores of 96.42％,87.82％and 98.64％,respectively,surpassing benchmarked models across various scales.Ablation experiments underscore the pivotal role of each component in achieving superior performance.关键词
自适应空间变换/Transformer/车辆检测/通道空间注意力机制/路侧相机Key words
adaptive spatial transformation/Transformer/vehicle detection/channel-space attention mechanism/roadside camera分类
信息技术与安全科学引用本文复制引用
华家宝,张京瑞,朱福民,陈璐..基于路侧相机的自适应空间变换车辆检测方法[J].计算机工程,2025,51(6):349-359,11.基金项目
上海市科技计划项目(22dz1204100). （22dz1204100）
