The ability of uncrewed aerial vehicles (UAVs) to hover, recognize, and localize ground targets is crucial for efficient and accurate intelligent low-altitude operations, such as material delivery, emergency rescue, and firefighting. This paper presents a technical solution for low-altitude UAV target recognition and search localization. The core algorithm is a RepViT-enhanced detection model, which integrates the Re-Parameterization Vision Transformer (RepViT) lightweight neural network with an efficient object detection framework, further augmented by the Convolutional Block Attention Module (CBAM) to improve detection accuracy. The search localization strategy implements a tiered approach for exploring nearby areas from the current position, assigning targets to priority tiers and visiting them in order of priority. Experimental results demonstrate that the RepViT-enhanced model achieves a mean average precision (mAP) of 98.58% on a custom emergency rescue dataset, improving real-time detection speed by two frames per second (18.70 FPS vs. 16.70 FPS for the standard YOLOv4 baseline). Thus, the proposed method effectively enhances both detection accuracy and speed, enabling better target search and localization in complex environments. The search strategy was validated through simulations, confirming its feasibility.
Li et al. (Wed,) studied this question.