A Lightweight Multi-Level Feature Fusion Detector for UAV-Based Tiny Personnel Detection in Hilly Road Safety Monitoring and Rescue

Target detection systems based on UAV platforms have the advantages of speed, flexibility, and agility in safety monitoring and rescue operations on hilly roads. However, due to the high altitude of aerial imaging, terrain occlusions, and interference from complex backgrounds, trapped individuals often appear as visually negligible small targets, leading to high miss rates and delayed responses in traditional detection methods. To address this urgent need, this paper proposes a lightweight small-target detector called FCML-YOLO. First, a frequency-domain feature enhancement (FDFE) module is designed, which extracts frequency-domain features using the discrete cosine transform and enhances global context perception through adaptive global pooling and multi-branch fully connected layers. Then, a content-aware reassembly of features (CARAFE) module is incorporated to preserve fine-grained image details during upsampling. Additionally, a multi-scale feature reconstruction (MSFR) module is developed, which integrates features from multiple scales and reduces redundant information using an adaptive weighting mechanism. Building on this, we construct a lightweight multi-level feature fusion (LMFF) network by removing redundant structures and fully exploiting deep and shallow features. The experimental results on multiple datasets demonstrate that, compared to YOLO11s, FCML-YOLO achieves 4.4% improvement in mAP50 on the self-built OPVM-VIRD dataset. Additionally, the model demonstrates a significant advantage over mainstream detection models on public datasets such as VisDrone, USOD, DOTA, and TinyPerson. Furthermore, experiments are extended to the search-and-rescue-oriented SARD dataset to verify the applicability of FCML-YOLO in UAV-based rescue scenarios. The model is deployed on a self-developed UAV-mounted detection pod system, with the number of parameters reduced by 62.6% compared to the baseline model, achieving real-time performance at 64 frames per second (FPS).