Detecting small objects from UAV-captured aerial imagery is a critical yet challenging task, hindered by factors such as small object size, complex backgrounds, and subtle inter-class differences. Single-modal methods lack the robustness for all-weather operation, while existing multimodal solutions are often too computationally expensive for deployment on resource-constrained UAVs. To this end, we propose TAF-YOLO, a lightweight and efficient multimodal detection framework designed to balance accuracy and efficiency. First, we propose an early fusion module, the Two-branch Adaptive Fusion Network (TAFNet), which adaptively integrates visible and infrared information at both pixel and channel levels before the feature extractor, maximizing complementary data while minimizing redundancy. Second, we propose a Large Adaptive Selective Kernel (LASK) module that dynamically expands the receptive field using multi-scale convolutions and spatial attention, preserving crucial details of small objects during downsampling. Finally, we present an optimized feature neck architecture that replaces PANet’s bidirectional path with a more efficient top-down pathway. This is enhanced by a Dual-Stream Attention Bridge (DSAB) that injects high-level semantics into low-level features, improving localization without significant computational overhead. On the VEDAI benchmark, TAF-YOLO achieves 67.2% mAP₅₀, outperforming the CFT model by 2.7% and demonstrating superior performance against seven other YOLO variants. Our work presents a practical and powerful solution that enables real-time, all-weather object detection on resource-constrained UAVs.

Detecting small objects from UAV-captured aerial imagery is a critical yet challenging task, hindered by factors such as small object size, complex backgrounds, and subtle inter-class differences. Single-modal methods lack the robustness for all-weather operation, while existing multimodal solutions are often too computationally expensive for deployment on resource-constrained UAVs. To this end, we propose TAF-YOLO, a lightweight and efficient multimodal detection framework designed to balance accuracy and efficiency. First, an early fusion module, the Two-branch Adaptive Fusion Network (TAFNet), which adaptively integrates visible and infrared information at both pixel and channel levels before the feature extractor, maximizing complementary data while minimizing redundancy. Second, a Large Adaptive Selective Kernel (LASK) module that dynamically expands the receptive field using multi-scale convolutions and spatial attention, preserving crucial details of small objects during downsampling. Finally, an optimized feature neck architecture that replaces PANet's bidirectional path with a more efficient top-down pathway. This is enhanced by a Dual-Stream Attention Bridge (DSAB) that injects high-level semantics into low-level features, improving localization without significant computational overhead. On the VEDAI benchmark, TAF-YOLO achieves 67.2% mAP50, outperforming the CFT model by 2.7% and demonstrating superior performance against seven other YOLO variants. Our work presents a practical and powerful solution that enables real-time, all-weather object detection on resource-constrained UAVs.

UAV (Unmanned Aerial Vehicle)-to-satellite self-localization is commonly treated as satellite tile retrieval. This makes large-area search tractable, but it also forces a continuous localization problem into a discrete ranking form. Once the task is defined this way, training naturally relies on hard positive–negative tile labels, and inference tends to read coordinates from the top-ranked tile center. The model, therefore, learns image identity more than geographic continuity, while the final estimate remains vulnerable to tile-center quantization and top-1 retrieval errors. We propose PFIRNet (Probability Field Inference Network), a continuous geographic posterior inference framework that reformulates retrieval outputs as evidence for coordinate estimation rather than discrete tile selection. It uses distance-aware geographic supervision to shape candidate responses according to metric proximity, lifts top-k candidates into a coordinate-space probability field, and applies risk-calibrated multi-peak verification to update the estimate only when an alternative posterior peak is sufficiently supported. On DenseUAV, PFIRNet reduces the median localization error to 9.84 m and outperforms both one-stage retrieval methods and two-stage matching baselines. It also remains more robust under sparse and non-aligned satellite galleries.