模糊场景下行人与车辆检测算法  

Pedestrian and Vehicle Detection Algorithm in Blurred Scenarios

作　　者：郑广海[1] 张海宁 曲英伟[1] ZHENG Guang-Hai;ZHANG Hai-Ning;QU Ying-Wei(School of Railway Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学轨道智能工程学院,大连116028

出　　处：《计算机系统应用》2025年第2期145-153,共9页Computer Systems & Applications

摘　　要：针对雾霾、雨雪等恶劣天气下拍摄到的图像退化模糊,难以进行准确识别与检测的问题,本文提出模糊场景下行人与车辆检测算法LiteBlurVisionNet(轻模糊视觉网络).在主干网络部分使用global context enhancer注意力改进轻量级MobileNetV3模块,减少了参数量,使得模型在雾霾、雨雪等恶劣天气条件下图像处理效率更高.颈部网络采用更为轻量化的Ghost模块和由Ghost bottleneck模块改进的spectral Ghost unit模块,能够更有效地捕获全局上下文信息,提高特征的区分度和表达能力,有助于减少参数数量和计算复杂度,从而提高网络处理速度和效率;预测部分采用DIoU NMS基于非极大抑制方法进行最大局部搜索,去除冗余的检测框,提高检测算法在模糊场景下的准确性.实验结果表明,LiteBlurVisionNet算法模型的参数量比RTDETR-ResNet50算法模型下降了96.8%,比YOLOv8n算法模型下降了55.5%,LiteBlurVisionNet算法模型的计算量比Faster R-CNN算法模型下降了99.9%,比YOLOv8n算法模型下降了57%,LiteBlurVisionNet算法模型的mAP0.5比IAL-YOLO算法模型提高了13.71%,比YOLOv5s算法模型提高了2.4%,这意味着模型在存储和计算方面更加高效,尤其适用于资源受限的环境或移动端设备.Aiming at degraded and blurred images captured under harsh weather conditions such as haze,rain,and snow,which make accurate recognition and detection challenging,this study proposes a pedestrian and vehicle detection algorithm,lightweight blur vision network(LiteBlurVisionNet),for blurred scenes.In the backbone network,the global context enhancer attention-improved lightweight MobileNetV3 module is used,reducing the number of parameters and making the model more efficient in image processing under harsh weather conditions such as haze and rain.The neck network adopts a lighter Ghost module and the spectral ghost unit module improved from the Ghost bottleneck module.These modules can more effectively capture global context information,improve the discrimination and expressive ability of features,help reduce the number of parameters and computational complexity,and thereby improve the network’s processing speed and efficiency.In the prediction part,DIoU NMS based on the non-maximum suppression method is used for maximum local search to remove redundant detection boxes and improve the accuracy of the detection algorithm in blurred scenes.Experimental results show that the parameter count of the LiteBlurVisionNet algorithm model is reduced by 96.8%compared to the RTDETR-ResNet50 algorithm model,and by 55.5%compared to the YOLOv8n algorithm model.The computational load of the LiteBlurVisionNet algorithm model is reduced by 99.9%compared to the Faster R-CNN algorithm model and by 57%compared to the YOLOv8n algorithm model.The mAP0.5 of the LiteBlurVisionNet algorithm model is improved by 13.71%compared to the IAL-YOLO algorithm model and by 2.4%compared to the YOLOv5s algorithm model.This means the model is more efficient in terms of storage and computation and is particularly suitable for resource-constrained environments or mobile devices.

关 键 词：MobileNetV3 模糊场景 人车检测 轻量化 LiteBlurVisionNet 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]