Abstract

Object detection in real-world scenarios is often challenged by adverse visual conditions, such as low illumination, strong glare, and dense fog, which severely degrade visible-spectrum features and lead to missed detections, inaccurate localization, and reduced detection accuracy. To address these issues, this paper proposes AQFS-Net, a dual-modal fusion detection network for visible-infrared object detection. Built upon YOLOv13, AQFS-Net adopts a symmetric dual-branch backbone by incorporating infrared images, thereby exploiting the complementary information between the visible and infrared modalities. To alleviate the negative transfer caused by conventional static fusion strategies, an Adaptive Quality-Aware Fusion Module (AQFM) is designed to dynamically enhance informative features and suppress degraded information according to the modality-specific reliability of different regions. In addition, a Foreground-Aware Saliency Guidance (FASG) branch is introduced to guide the network to focus on target regions through foreground supervision, reducing interference from complex backgrounds. Experimental results on the public LLVIP and M3FD datasets show that the proposed method improves mAP@0.5 by 6.8 and 3.1 percentage points, respectively, compared with the baseline using only visible images. These results demonstrate the effectiveness of AQFS-Net in improving dual-modal fusion quality and detection performance under challenging visual conditions, providing a practical reference for visible-infrared object detection in complex illumination scenarios.

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