东南大学学报（自然科学版）2024，Vol.54Issue(4)：1005-1013,9.DOI:10.3969/j.issn.1001-0505.2024.04.025
基于高效卷积注意力特征融合的道路目标检测
Object detection in road based on efficient convolutional attention feature fusion
摘要
Abstract
A lightweight object detection model based on efficient convolutional attention feature fusion was proposed to address the issues of large number of parameters and feature scale differences in the YOLOv5s benchmark model.Firstly,a lightweight feature extraction module based on phantom operation was construc-ted to improve the real-time performance of the model while ensuring detection accuracy close to the original model.Secondly,the channel attention and spatial attention modules were optimized,and an attention feature fusion module based on efficient convolution was proposed.Meanwhile,a lightweight object detection model with high detection accuracy and real-time performance was designed.Experiments were conducted on the dataset BDD100K with different complex road scenes.The results show that the designed model is improved in detection accuracy and inference speed compared with the benchmark model.The average detection accuracy of the entire class is improved by 1.4％,and the frame rate is improved by 28.2％.Compared with main-stream deep learning models in current industry applications,the proposed model shows significant advantages in the balance between accuracy and speed.关键词
目标检测/轻量化/注意力特征融合/注意力机制Key words
object detection/lightweight/attention feature fusion/attention mechanism分类
交通工程引用本文复制引用
罗为明,李旭,孙正良,袁建华,朱建潇,王贲武..基于高效卷积注意力特征融合的道路目标检测[J].东南大学学报（自然科学版）,2024,54(4):1005-1013,9.基金项目
国家重点研发计划资助项目(2021YFF0602703). （2021YFF0602703）
