In this article, we propose an effective infrared and visible image fusion network based on contrastive learning, which is called CLF-Net. A novel noise contrastive estimation framework is introduced into the image fusion to maximize mutual information between the fused image and source images. First, an unsupervised contrastive learning framework is constructed to promote fused image selectively retaining the most similar features in local areas of different source images. Second, we design a robust contrastive loss based on the deep representations of images, combining with the structural similarity loss to effectively guide the network in extracting and reconstructing features. Specifically, based on the deep representation similarities and structural similarities between the fused image and source images, the loss functions can guide the feature extraction network in adaptively obtaining the salient targets of infrared images and background textures of visible images. Then, the features are reconstructed in the most appropriate manner. In addition, our method is an unsupervised end-to-end model. All of our methods have been tested on public datasets. Based on extensive qualitative and quantitative analysis results, it has been demonstrated that our proposed method performs better than the existing state-of-the-art fusion methods.

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs.

Visible-Infrared (RGB-IR) object detection plays a crucial role in UAV-based vision tasks. However, existing methods still suffer from learning bias caused by imbalanced information distribution and inaccurate fusion due to modal conflicts. Inspired by the human multisensory information processing mechanism, we propose a novel “CompensationFusion” progressive detection framework, CCSFuse, to fully exploit the complementary relationship between modalities while eliminating conflict interference. Specifically, we design a cross-modal feature compensation module, which establishes inter-modal information interaction to achieve mutual complementarity and enhancement during feature extraction, effectively mitigating the issue of imbalanced modal information distribution. Additionally, we introduce an adaptive feature-selection fusion module to address modal conflicts. We employ a cross-modal channel attention to calibrate channel features of different modalities and utilizes a selective fusion strategy to dynamically assess modal importance, thereby achieving adaptive modal fusion. Finally, we validate the effectiveness of CCSFuse on the DroneVehicle and LLVIP datasets. The results confirm that CCSFuse significantly improves the efficiency of feature optimization and integration. In UAV-based object detection scenarios, CCSFuse outperforms state-of-the-art methods in both qualitative and quantitative comparisons, particularly for small objects and low-quality modalities. The code is available at https://github.com/ZhangT-xxl/CCSFuse.

The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers.

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Infrared object detection is an important prerequisite for small unmanned aerial vehicle (UAV) swarm countermeasures. Owing to the limited imaging area and texture features of small UAV targets, accurate infrared detection of UAV swarm targets is challenging. In this paper, the GM-DETR is proposed for the detection of densely distributed small UAV swarm targets in infrared scenarios. Specifically, high-level and low-level features are fused by the Fine-Grained Context-Aware Fusion module, which augments texture features in the fused feature map. Furthermore, a Supervised Sampling and Sparsification module is proposed as an explicit guiding mechanism, which assists the GM-DETR to focus on high-quality queries according to the confidence value. The Geometric Relation Encoder is introduced to encode geometric relation among queries, which makes up for the information loss caused by query serialization. In the second stage of the GM-DETR, a long-term memory mechanism is introduced to make UAV detection more stable and distinguishable in motion blur scenes. In the decoder, the self-attention mechanism is improved by introducing memory blocks as additional decoding information, which enhances the robustness of the GM-DETR. In addition, we constructed a small UAV swarm dataset, UAV Swarm Dataset (USD), which comprises 7000 infrared images of low-altitude UAV swarms, as another contribution. The experimental results on the USD show that the GM-DETR outperforms other state-of-the-arts detectors and obtains the best scores (90.6 on ${\mathrm{AP}}_{75}$ and 63.8 on ${\mathrm{AP}}_{\mathit{S}}$), which demonstrates the effectiveness of the GM-DETR in detecting small UAV targets. The good performance of the GM-DETR on the Drone Vehicle dataset also demonstrates the superiority of the proposed modules in detecting small targets.