UAVs are widely used for all-weather, round-the-clock security inspections in urban and industrial areas. However, pure visible-light systems fail at night or in adverse weather conditions, while pure infrared methods are limited by thermal noise, low spatial resolutions, and high false alarm rates. Multispectral images render the task of object detection highly reliable and robust by providing complementary target feature information. This study suggests a frequency-based cross-attention transformer (FCAT) for multispectral object detection as a solution to this issue. This approach collects cross-modal complementary characteristics, effectively learns and integrates global contextual information via the cross-attention mechanism, and greatly increases multispectral object detection accuracy. At the same time, spatial-domain features are mapped to the frequency domain via the Fourier transform, and the scaled dot product attention is estimated via element-wise product operations, which break through the limitation of traditional spatial-domain matrix multiplication and effectively reduce the computational cost of the model. Additionally, this study independently builds a multi-scene multi-time climate visible–infrared dataset (OPVM-VIRD), which contains 20,025 target instances, to address the issue of the lack of all-weather cross-spectral data in object detection tasks from the perspective of UAVs. Experimental findings from the OPVM-VIRD, M3FD, and FLIR datasets demonstrate that our proposed approach outperforms prevailing state-of-the-art multispectral object detection algorithms on public benchmarks, while the FCAT model achieves an mAP50 score of 94.7% on our custom-built dataset—10.8% higher than ICAF. At the same time, the number of FCAT parameters is 85.26 M, which is significantly lower than that of mainstream models, such as ICAF. Therefore, the FCAT is a change detection strategy with strong model generalization abilities, and it has important application value in the all-day and all-weather security patrol of cities and industrial parks carried out by UAVs.

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.