同济大学学报（自然科学版）2024，Vol.52Issue(4)：532-540,9.DOI:10.11908/j.issn.0253-374x.23399
基于自适应融合的实时车辆检测
Real-time Vehicle Detection Based on Adaptive Fusion
摘要
Abstract
A traffic target detection algorithm,fusion attention adaptive pyramid network(FAAP-Net),is proposed to address the issues of slow speed and low accuracy in traditional vehicle detection techniques,significantly reducing the occurrence of traffic accidents.To mitigate computational complexity,a lightweight complementary pooling structure(CPS)is designed,employing two sets of different pooling combinations in width and height,which maintains a high precision while significantly reducing the floating point operations per second(GFLOPs)and the parameter count of the network.Addressing the information loss during intelligent traffic system feature map generation,the adaptive fusion feature pyramid network(AF-FPN)incorporates the adaptive attention module(AAM)and the feature enhancement module(FEM)to integrate shape features for vehicle detection.Lastly,to address the weak representation of vehicle detail features,a channel-wise grouped attention(SA)mechanism is introduced,enhancing the focus of the backbone network on various vehicle detection details and effectively extracting significant features.The experimental results on the BDD100K dataset demonstrate that the FAAP-Net algorithm achieves a notable improvement,increasing the average precision from 30.3%to 43.7%.关键词
目标检测/车辆检测/互补池化/自适应融合/通道维度分组注意力Key words
object detection/vehicle detection/complementary pooling/adaptive fusion/shuffle attention分类
信息技术与安全科学引用本文复制引用
陈婷,朱熟康,高涛,李浩,涂辉招,李子琦..基于自适应融合的实时车辆检测[J].同济大学学报（自然科学版）,2024,52(4):532-540,9.基金项目
国家重点研发计划(2023YFB2504703,2019YFE0108300) （2023YFB2504703,2019YFE0108300）
国家自然科学基金(52172379,62001058) （52172379,62001058）
中央高校基本科研业务费专项资金(300102241201,310833160212) （300102241201,310833160212）
