Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

In the field of remote sensing, cloud and cloud shadow will result in optical remote sensing image contamination, particularly high cloud cover, which will result in the complete loss of certain ground object information. The presence of thick cloud severely limits the use of optical images in production and scientific research, so it is critical to conduct further research into removing the thick cloud occlusion in optical images to improve the utilization rate of optical images. The state-of-the-art cloud removal methods proposed are largely based on convolutional neural network (CNN). However, due to CNN’s inability to gather global content information, those cloud removal approaches cannot be improved further. Inspired by the transformer and multisource image fusion cloud removal method, we propose a transformer-based cloud removal method (Former-CR), which directly reconstructs cloudless images from SAR images and cloudy optical images. The transformer-based model can efficiently extract and fuse global and local context information in SAR and optical images, generating high-quality cloudless images with higher global consistency. In order to enhance the global structure, local details, and visual effect of the reconstructed image, we design a new loss function to guide the image reconstruction. A comparison with several SAR-based cloud removal methods through qualitative and quantitative experimental evaluation on the SEN12MS-CR dataset demonstrates that our proposed method is effective and superior.

Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image interpretation, it often prioritizes recognition over computational efficiency and underutilizes SAR image prior information. Therefore, this paper proposes YOLOv5s-based ship detection in SAR images. Firstly, for comprehensive detection enhancement, we employ the lightweight YOLOv5s model as the baseline. Secondly, we introduce a sub-net into YOLOv5s, learning traditional features to augment ship feature representation of Constant False Alarm Rate (CFAR). Additionally, we attempt to incorporate frequency-domain information into the channel attention mechanism to further improve detection. Extensive experiments on the Ship Recognition and Detection Dataset (SRSDDv1.0) in complex SAR scenarios confirm our method’s 68.04% detection accuracy and 60.25% recall, with a compact 18.51 M model size. Our network surpasses peers in mAP, F1 score, model size, and inference speed, displaying robustness across diverse complex scenes.

In the realm of intelligent transportation systems, Automatic License Plate Recognition (ALPR) is integral, yet is often hindered by heavy computational loads and massive memory footprints. Existing methods either enhance recognition accuracy through complex models or minimize resource requirements by utilizing low-performance models and low-resolution inputs, both of which pose challenges in real-world applications. To address these issues, this paper introduces a novel ultra-lightweight ALPR system specifically designed for deployment on microcontrollers, offering a cost-effective and energy-efficient solution for large-scale applications. We simplify the process by decoupling ALPR into three sub-tasks: detection, localization, and recognition. For each sub-task, specific improvement strategies such as Residual Log-likelihood Estimation (RLE)-based keypoint localization, adaptive data augmentation, and multi-voting recognition mechanism are designed. Our proposed system naturally mitigates the issues of large perspective transformations frequently encountered in real-world settings. As a supplementary contribution, we introduce and release the TJLP dataset. Practical application and superior performance of our method have been demonstrated on resource-limited devices like RV1106 (ARM Cortex-A7 with a 0.5 TOPS NPU), achieving an inference speed of 22 FPS with a power consumption of 629 mW. Notably, our fully optimized model exhibits a recognition accuracy of 99.23% in real-world environments.

Human activity recognition (HAR) in complex scenarios, particularly in through-wall environments, represents a significant frontier in sensing technology research. The implementation of reliable HAR systems faces substantial challenges due to severe noise interference, limited sensor penetration capability, and unpredictable human motion patterns. This work presents two main contributions to address these challenges. First, we develop an integrated HAR system that combines advanced radar signal processing with deep learning approaches. The system features two key innovations: the usage of compact multi-input multi-output (MIMO) ultra-wideband (UWB) radar for capturing multidimensional echo sequences, and a proposed MobRNet architecture that enables efficient feature extraction through integrated radar modules. Second, to systematically evaluate wall obstruction effects on UWB radar data, we establish a comprehensive radar dataset where wall presence serves as the controlled environmental variable with different materials and thickness. The dataset comprises 29,900 radar feature images collected from real indoor environments with various wall materials and thicknesses, accompanied by corresponding video data for validation. Experimental results demonstrate that the proposed system achieves a recognition accuracy of 99% on the total dataset and maintains robust performance with previously unseen subjects, representing an 8% improvement in comprehensive recognition performance compared to state-of-the-art methods.

When the SLAM algorithm is used to provide positioning services for a robot in an indoor scene, dynamic obstacles can interfere with the robot’s observation. Observing the ceiling using an upward-looking camera that has a stable field of view can help the robot avoid the disturbance created by dynamic obstacles. Aiming at the indoor environment, we propose a new ceiling-view visual odometry method that introduces plane constraints as additional conditions. By exploiting the coplanar structural constraints of the features, our method achieves better accuracy and stability in a ceiling scene with repeated texture. Given a series of ceiling images, we first use the semi-direct method with the coplanar constraint to preliminarily calculate the relative pose between camera frames and then exploit the ceiling plane as an additional constraint. In this step, the photometric error and the geometric constraint are both optimized in a sliding window to further improve the trajectory accuracy. Due to the lack of datasets for ceiling scenes, we also present a dataset for the ceiling-view visual odometry for which the LiDAR-Inertial SLAM method provides the ground truth. Finally, through an actual scene test, we verify that, in the ceiling environment, our method performs better than the existing visual odometry approach.