Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration.

Object detection in Synthetic Aperture Radar (SAR) images remains a challenging task due to factors such as complex backgrounds, frequent fluctuations in object scale and quantity, and the inherent discrete scattering characteristics of SAR imaging. To address these challenges, we propose a DETR (DEtection TRansformer) with Corner-Guided and Adaptive Query for SAR Object Detection, which integrates a Corner-Guided Multi-Scale Feature Enhancement Module (CMFE) and an Adaptive Query Regression Module (AQR). The CMFE module processes multi-scale features by detecting and clustering corners to assess the scale and quantity of objects, which are used to compute the importance weights of features at different scales. The AQR module regresses the number of object queries by evaluating the rough object count from the low-level features, thereby achieving more precise and adaptive query allocation. Both modules are supervised by real data. Extensive experiments conducted on the SARDet-100K and FAIR-CSAR datasets demonstrate that our method achieves SOTA (state-of-the-art) performance, and achieved mAP@50 scores of 69.8% and 92.9%, validating its effectiveness and practical applicability in SAR object detection.

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets.

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.

Synthetic Aperture Radar (SAR) object detection often suffers from speckle noise and deformation of diverse target shapes, leading to an inability for the algorithm to effectively distinguish between foreground and background. To address these challenges, we propose a dual-branch framework with a segmentation surrogate task that leverages foreground and background cues to guide robust multi-scale feature extraction. Our method comprises three key components: (1) a U-Net guided Dual-Branch Architecture with Cross-Attention Module (UDC), which introduces a U-Net-based segmentation branch to generate “region-based hints” for cross-attention fusion with the detection branch; (2) Shi-Tomasi corner detection with Convex polygon optimization (STC) for pseudo mask generation, enabling cost-effective segmentation masks from bounding label information, alleviating the need for exhaustive pixel-level annotations; and (3) a Two-Stage Training Strategy, which first jointly trains both branches, then freezes segmentation branch to finetune the detection backbone, mitigating gradient conflicts. Extensive experiments on remote sensing dataset, SARDet-100k, validate the detection superiority of our framework (+4.2% mAP), especially in medium (+3.9% mAP) and small target detection (+5.2% mAP). Our findings highlight the importance of task-specific priors in SAR detection pipelines and confirm that combining pseudo-labeled segmentation with a targeted training procedure within our proposed framework yields robust, scalable performance for SAR object detection.