MHF-Net: Multi-Modal Residual Fusion Object Detection Network Based on High-Frequency Gated Convolution

Abstract

This paper aims to address image degradation caused by adverse weather conditions and partial occlusion in autonomous driving scenarios. The network adopts a dual-branch architecture, which utilizes a residual fusion block (ResFuse) to achieve the deep fusion of cross-modal features. A dual-path attention mechanism (DualAtt) and an integrated high-frequency attention module (HFAtt) were designed to capture key information in feature maps. Additionally, this study designs a spatial-channel downsampling module (SCDown). This module implements spatial-to-channel image downsampling. Experimental results demonstrate that the proposed network achieves a detection speed of 61 frames per second (FPS) and precision (P) of up to 87.4% on the M3FD dataset. Comparative experimental results show that MHF-Net achieves 84.3% in the mAP50 evaluation metric. Compared to the baseline model, the number of parameters was reduced by 95% and the computational complexity was reduced by 88%. Compared with other mainstream object detection algorithms, MHF-Net strikes a favorable balance regarding performance in autonomous driving scenarios.