复杂场景下的交通标志小目标检测算法  

Small target detection algorithm for traffic signs in complex scenes

作　　者：王浩 张其猛 龚德成 Wang Hao;Zhang Qimeng;Gong Decheng(School of Computer Science and Information Engineering,Shanghai Institute of Technology,Shanghai 201418,China)

机构地区：[1]上海应用技术大学计算机科学与信息工程学院,上海201418

出　　处：《电子测量技术》2025年第2期158-169,共12页Electronic Measurement Technology

基　　金：国家自然科学基金(52402381)项目资助。

摘　　要：在交通标志识别应用中,待检测目标多为小目标,易出现漏检、误检等问题。针对这些问题,基于YOLOv8s算法设计了一种改进的交通标志识别算法,FKDS-YOLOv8s。使用FasterBlock重构C2f模块,形成新的轻量化模块C2f-Faster,既提升模型特征提取能力,又降低了计算开销;基于SENet和ResNeXt模型设计一种新的检测头Detect_SR,使模型能够有效地聚焦于小目标的关键特征;融入轻量且高效的动态上采样器DySample,显著减少了GPU内存消耗;通过增加上采样和Prediction输出层次,模型能够捕捉丰富的位置信息,有效解决了YOLOv8s模型在处理小目标时信息不足的问题;引入Shape-IoU损失函数,优化了原CIoU在边框回归中的不足;此外,在Neck部分融入了本文新设计的注意力机制DKN-Attention,在上采样和下采样过程中定位微小物体场景的注意力区域,提升了远处小型交通标志的特征提取和识别能力。实验在中国交通标志数据集TT100K上进行,结果表明,FKDS-YOLOv8s相比基准模型,在查准率(P)、查全率(R)和mAP50上分别提升了5.9%、4.2%和6.3%。较传统方法,FKDS-YOLOv8s在性能上表现出显著优势。In the application of traffic sign recognition,most of the targets to be detected are small targets,which are prone to problems such as missed detection and false detection.In order to solve these problems,an improved traffic sign recognition algorithm,FKDS-YOLOv8s,was designed based on the YOLOv8s algorithm.FasterBlock is used to reconstruct the C2f module to form a new lightweight module C2f-Faster,which not only improves the feature extraction ability of the model,but also reduces the computational overhead.Based on the SENet and ResNeXt models,a new detection head Detect_SR was designed to enable the model to effectively focus on the key features of small targets.DySample,a lightweight and efficient dynamic upsampler,significantly reduces GPU memory consumption.By increasing the output level of upsampling and prediction,the model can capture rich position information,which effectively solves the problem of insufficient information when the YOLOv8s model processes small targets.The Shape-IoU loss function is introduced to optimize the shortcomings of the original CIoU in the border regression.In addition,the newly designed attention mechanism DKN-Attention is integrated into the Neck part,which locates the attention area of the scene of small objects during the upsampling and downsampling process,and improves the feature extraction and recognition ability of small traffic signs in the distance.The experimental results were carried out on the Chinese traffic sign dataset TT100K,and the results showed that FKDS-YOLOv8s improved the accuracy(P)、recall rate(R)and mAP50 by 5.9%、4.2%and 6.3%,respectively,compared with the benchmark model.Compared with the traditional method,FKDS-YOLOv8s shows significant advantages in performance.

关 键 词：交通标志识别 动态上采样 注意力机制 轻量化 Shape-IoU 

分 类 号：U491[交通运输工程—交通运输规划与管理] TP391[交通运输工程—道路与铁道工程] TN919.82[自动化与计算机技术—计算机应用技术]