Object detection in road based on efficient convolutional attention feature fusion

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 constructed 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 mainstream deep learning models in current industry applications,the proposed model shows significant advantages in the balance between accuracy and speed. © 2024 Southeast University. All rights reserved.