Weiwei Tang

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.

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).

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.

In this paper, a target detection network algorithm based on a multi-branch convolution and attention improvement Cross-Stage Partial-Fusion Bottleneck with Two Convolutions (C2F) module is proposed for the difficult task of detecting small targets in unmanned aerial vehicles. A C2F module method consisting of fusing partial convolutional (PConv) layers was designed to improve the speed and efficiency of extracting features, and a method consisting of combining multi-scale feature fusion with a channel space attention mechanism was applied in the neck network. An FA-Block module was designed to improve feature fusion and attention to small targets’ features; this design increases the size of the miniscule target layer, allowing richer feature information about the small targets to be retained. Finally, the lightweight up-sampling operator Content-Aware ReAssembly of Features was used to replace the original up-sampling method to expand the network’s sensory field. Experimental tests were conducted on a self-complied mountain pedestrian dataset and the public VisDrone dataset. Compared with the base algorithm, the improved algorithm improved the mAP50, mAP50-95, P-value, and R-value by 2.8%, 3.5%, 2.3%, and 0.2%, respectively, on the Mountain Pedestrian dataset and the mAP50, mAP50-95, P-value, and R-value by 9.2%, 6.4%, 7.7%, and 7.6%, respectively, on the VisDrone dataset.

The filter bank multi-carrier (FBMC) enabled visible light communication (VLC) systems based on the well-designed prototype filter with excellent time-frequency focusing characteristics, exhibit higher spectral efficiency and lower out-of-band spectral leakage than orthogonal frequency-division multiplexing. Asymmetrical clipping-aided hybrid optical FBMC (ACHO-FBMC) is a spectral- and power-efficient modulation scheme for VLC systems. However, the ACHO-FBMC cannot avoid the high signal peak-to-average power ratio (PAPR) problem and suffers power fading induced by limited modulation bandwidth. In this letter, we propose a real-valued precoded ACHO-FBMC modulation scheme to reduce signal PAPR and equalize subcarrier signal-to-noise ratio (SNR) so as to improve transmission performance. The simulation results revealed that the precoding techniques can reduce signal PAPR up to 4.7 dB at the complementary cumulative distribution function of 1e-4. Besides, the transmission performance of the 1.21-Gbps precoded FBMC signals was experimentally investigated in a blue light emitting diode-based VLC system with a 1-m wireless link. It indicated that the subcarrier SNR was well equalized with the proposed precoding techniques. Moreover, the bit error rate was reduced by about an order of magnitude.

Aircraft wake is a pair of counter-rotating vortices generated behind the aircraft, which can greatly impact the safety of fast takeoff and landing of aircraft and limit the improvement of airport capacity. The current wake parameter retrieval methods cannot locate the wake vortex's position and estimate its strength level in real time. To deal with this issue, a novel algorithm based on YOLOv5s deep learning network is proposed. The new algorithm establishes single vortex locating concept to adapt the wake vortex's evolution at complicate background wind field conditions, and proposes strength-based classification standard which can represent the real-time hazard of wake vortex to shorten the takeoff and landing intervals. Meanwhile, EIOU loss function is introduced to improve the precision of YOLOv5s network. Compared with the state-of-the-art object detection approaches, such as Cascade R-CNN, FCOS, and YOLOv5l, the superiority of new method is demonstrated in terms of accuracy and robustness by using the field detection data from Hong Kong International Airport.