一种增强前景的轻量级交通标志检测模型  

A Lightweight Traffic Sign Detection Model with Enhanced Foregrounds

作　　者：袁亚剑 毛力 YUAN Yajian;MAO Li(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,Jiangsu,China;Institute of Advanced Technology,Jiangnan University,Wuxi 214122,Jiangsu,China;Jiangsu Engineering Laboratory of Pattern Recognition and Computer Intelligence,Jiangnan University,Wuxi 214122,Jiangsu,China)

机构地区：[1]江南大学人工智能与计算机学院,江苏无锡214122 [2]江南大学先进技术研究院,江苏无锡214122 [3]江南大学江苏省模式识别与计算机智能工程实验室,江苏无锡214122

出　　处：《计算机工程》2025年第3期54-63,共10页Computer Engineering

基　　金：国家自然科学基金面上项目(62272202)。

摘　　要：交通标志检测在辅助驾驶中扮演着不可或缺的角色,为安全驾驶提供了至关重要的支持。在实际交通环境中,在黑夜或雨天产生的背景噪声会加大交通标志检测的难度。现有模型往往难以有效检出远处的小目标交通标志,此外,在设计交通标志检测模型时应当考虑到实际部署对模型体积的要求。为此,在YOLOv8的基础上提出一种增强前景的轻量级交通标志目标检测模型。首先,设计了1个轻量级的PC2f模块替换掉原本Backbone中的部分C2f模块,该模块降低了模型的参数量和计算量,在保留更多浅层信息的同时进一步丰富了梯度流信息,同时实现了模型轻量化和提升检测性能;其次,设计了前景增强模块(FEM)并将其引入Neck位置,该模块能够有效放大前景信息并减弱背景噪声;最后,增加了一层小目标检测层,用于在高分辨率的图像上提取浅层特征,加强模型对小目标交通标志的检测性能。实验结果表明,优化后的模型在数据集CCTSDB 2021和GTSDB上的mAP_(50)分别达到了82.5%和95.3%,相较于原模型分别提升了3.6和1百分点,并且模型权重大小减小了0.22×10^(6)。这些结果验证了所提模型在实际应用中的有效性。Traffic sign detection is crucial for assisted driving and plays a vital role in ensuring driving safety.However,in real-world traffic environments,factors such as darkness and rain create background noise that complicates the detection process.In addition,existing models often struggle to effectively detect small traffic signs from a distance.Furthermore,when a traffic sign detection model is designed,the model size must be considered for practical deployment.To address these challenges,this study proposes a lightweight traffic sign detection model based on YOLOv8 with enhanced foregrounds.First,a lightweight PC2f module is designed to replace a part of the C2f module in the original Backbone.This modification reduces the number of parameters and computational load,enriches the gradient flow,retains more shallow information,and ultimately enhances detection performance while maintaining a lightweight design.Next,the study designs a Foreground Enhancement Module(FEM)and incorporates it into the Neck position to effectively amplify the foreground information and reduce background noise.Finally,the study adds a small-target detection layer to extract shallow features from high-resolution images,thereby improving the ability of the model to detect small-target traffic signs.Experimental results show that the optimized model achieves a mAP_(50)of 82.5%and 95.3%on the CCTSDB 2021 and GTSDB datasets,which is an improvement of 3.6 and 1 percentage points over the original model,respectively,with a reduction in model weight size by 0.22×10^(6).These results confirm the effectiveness of the proposed model for practical applications.

关 键 词：交通标志检测 轻量化网络 前景增强模块 小目标检测 黑夜场景目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]