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).

Unmanned aerial vehicles (UAVs) frequently operate under complex illumination conditions during urban and industrial security patrols, including low light, fog, and strong backlighting. These conditions severely degrade the performance of existing dual-modal object detection methods. To overcome this limitation, we introduce a lightweight dual-modal object detection algorithm called M²CA-FFM, built upon multi-directional Mamba cross-attention feature fusion. First, a multi-directional Mamba enhancement module is developed to model spatial feature sequences via four-directional scanning. Second, a dedicated spectral enhancement module is designed to process spectral components in a differentiated manner. Together, these two modules enhance single-modal features from both the spatial and frequency domains, leading to improved feature robustness. For dual-modal object detection, we propose a Mixed Scanning Fusion Module that dynamically generates scanning paths focused on the target regions. This dynamic mechanism complements the fixed four-direction scanning and strengthens global modeling, enabling efficient fusion and deep complementarity between visible light and infrared features. Extensive experiments are conducted on a self-built dataset (OPVM-VIRD) and three public benchmarks (FLIR, M3FD, and VEDAI). Our method consistently outperforms current mainstream dual-modal detection algorithms across all datasets. On the OPVM-VIRD dataset, it achieves an mAP50 of 95.7%, which is 2.2% higher than the best result obtained with other fusion methods. Preliminary verification on the edge computing platform Atlas 200I DK A2 shows that the algorithm has significant lightweight advantages, with a parameter volume of only 72.07 M and a computational complexity of 134.41 GFLOPs, demonstrating the reasoning potential to meet real-time processing requirements. Overall, the proposed M²CA-FFM method offers a real-time detection strategy with strong generalization capabilities, holding significant practical value for around-the-clock security patrols in urban and industrial parks.