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.

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.

Positional encoding is crucial for the Transformer to effectively process multi-modal feature information in multispectral object detection. However, existing studies often directly apply single-modal encoding designs to multi-modal tasks, resulting in positional encoding focusing on spatial information while neglecting modality-specific information, which limits the performance of the Transformer. To explore the potential of positional encoding in jointly processing spatial and modality information in the multi-modal domain, we propose AMRoFormer, an adaptive multi-modal rotary position embedding Transformer for multispectral remote sensing object detection. It efficiently exploits multi-modal spatial and modality information. The AMRo-Former consists of the multi-modal rotary position embedding cross attention (MRoCA) and adaptive inter-modal spacing module (AISM), where MRoCA includes multi-modal rotary position embedding (MRoPE) and cross-attention mechanism. We introduce a 3D positional encoding strategy that addresses the challenges associated with 2D positional encoding, particularly the difficulty of retaining modality-specific information. We design MRoPE by applying the 3D positional encoding strategy and rotary position embedding (RoPE), which enables the independent processing of modality information through separate 2D encoding and mixed 3D encoding. Based on MRoPE, we further introduce MRoCA, the first multi-modal attention mechanism to integrate a 3D positional encoding strategy and to introduce the concept of inter-modal spacing. To better exploit inter-modal spacing while reducing the costs associated with manually setting, we design a lightweight AISM. Integrated into a dual-stream backbone network, our method achieved state-of-the-art (SOTA) performance in multispectral object detection, attaining the highest detection evaluation metrics to date on the VEDAI, Drone-Vehicle and LLVIP datasets, with mAP50 scores of 0.8962, 0.8531 and 0.9937. Overall, AMRo-Former demonstrates exceptional generalization performance in multispectral object detection.