With the rapid growth of the global drone market, a variety of small drones have posed a certain threat to public safety. Therefore, we need to detect small drones in a timely manner so as to take effective countermeasures. At present, the method based on deep learning has made a great breakthrough in the field of target detection, but it is not good at detecting small drones. In order to solve the above problems, we proposed the IRSDD-YOLOv5 model, which is based on the current advanced detector YOLOv5. Firstly, in the feature extraction stage, we designed an infrared small target detection module (IRSTDM) suitable for the infrared recognition of small drones, which extracted and retained the target details to allow IRSDD-YOLOv5 to effectively detect small targets. Secondly, in the target prediction stage, we used the small target prediction head (PH) to complete the prediction of the prior information output via the infrared small target detection module (IRSTDM). We optimized the loss function by calculating the distance between the true box and the predicted box to improve the detection performance of the algorithm. In addition, we constructed a single-frame infrared drone detection dataset (SIDD), annotated at pixel level, and published an SIDD dataset publicly. According to some real scenes of drone invasion, we divided four scenes in the dataset: the city, sky, mountain and sea. We used mainstream instance segmentation algorithms (Blendmask, BoxInst, etc.) to train and evaluate the performances of the four parts of the dataset, respectively. The experimental results show that the proposed algorithm demonstrates good performance. The $A{P}_{50}$ measurements of IRSDD-YOLOv5 in the mountain scene and ocean scene reached peak values of 79.8% and 93.4%, respectively, which are increases of 3.8% and 4% compared with YOLOv5. We also made a theoretical analysis of the detection accuracy of different scenarios in the dataset.

The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.

Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and thus still face limitations in accuracy and generalization capability. To overcome these limitations, this paper proposed the MSTAN, which consists of a multi-scale Transformer encoder and a decoder centered on a four-layer ASFF module. The four-layer ASFF module dynamically learns spatially adaptive weights to capture multi-scale semantic information. Comparative experiments demonstrate that MSTAN achieves high-precision change detection. Cross-dataset evaluation experiments demonstrate that MSTAN possesses strong generalization ability. Ablation experiments confirm the effectiveness of the four-layer ASFF module in fusing multi-scale features. Complexity analysis quantifies the computational overhead of the four-layer ASFF module. These results highlight MSTAN’s powerful generalization capability and its promising potential for change detection tasks.

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained.