COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection

• URL: http://arxiv.org/abs/2508.09533v1
• Date: Wed, 13 Aug 2025 06:30:03 GMT
• Title: COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection
• Authors: Peiran Peng, Tingfa Xu, Liqiang Song, Mengqi Zhu, Yuqiang Fang, Jianan Li,
• Abstract summary: We propose a novel framework for tiny object detection in multimodal Red-Green-Blue-Thermal (RGBT) imagery.<n>Cross-Layer Fusion Module fuses high-level visible and low-level thermal features for enhanced semantic and spatial accuracy.<n>Dynamic Alignment and Scale Refinement module corrects cross-modal spatial misalignments.<n>GeoShape Similarity Measure is used for better localization.
• Score: 13.236592868442678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveillance, search and rescue, and autonomous navigation. Drone-based scenarios exacerbate these challenges due to spatial misalignment, low-light conditions, occlusion, and cluttered backgrounds. Current methods struggle to leverage the complementary information between visible and thermal modalities effectively. We propose COXNet, a novel framework for RGBT tiny object detection, addressing these issues through three core innovations: i) the Cross-Layer Fusion Module, fusing high-level visible and low-level thermal features for enhanced semantic and spatial accuracy; ii) the Dynamic Alignment and Scale Refinement module, correcting cross-modal spatial misalignments and preserving multi-scale features; and iii) an optimized label assignment strategy using the GeoShape Similarity Measure for better localization. COXNet achieves a 3.32\% mAP$_{50}$ improvement on the RGBTDronePerson dataset over state-of-the-art methods, demonstrating its effectiveness for robust detection in complex environments.

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