ArticleSensors (Basel, Switzerland)2026
MHF-Net: Multi-Modal Residual Fusion Object Detection Network Based on High-Frequency Gated Convolution.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors.
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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 mAP
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.