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.

Background: The aim of this study was to compare the efficacy of ultrasound-guided PENG (pericapsular nerve group) block and drug therapy with intravenous flurbiprofen for early analgesia in elderly patients with hip fractures after hospitalization. Methods: This is a single-center, observer-blinded, prospective, randomized, controlled trial. A total of 41 elderly patients (aged 60 or older) with hip fractures were enrolled in the current study. Patients were randomly assigned to two groups: Group P (ultrasound-guided PENG block, 20 mL of 0.375% ropivacaine) and Group F (intravenous flurbiprofen 50 mg). The primary outcome measure was the dynamic (passive straight leg raising 15°) NRS (numerical rating scale 0 to 10) pain scores at different time points. The secondary outcomes were the static NRS scores at different time points, the number of rescue analgesia sessions, patient satisfaction, and the incidence of complications. Results: Patients in the two groups had comparable baseline characteristics. The group P had lower dynamic and static NRS scores at 15 min, 30 min, 6 h, and 12 h after intervention (P<0.05) than the group F. The highest NRS pain scores in the group P were still lower than the NRS scores in the group F at 30 min-12 h (Group F: 5.57±1.54 vs. Group P: 3.00±1.12, P0.05). The group P had higher satisfaction scores (Group P: 9 (9,9) vs. Group F: 8 (7,8), P<0.001). There was no statistically significant difference in the number of rescue analgesics at 0-12 h or 12-24 h or the incidence of complications between the groups. Conclusions: Compared with intravenous flurbiprofen, ultrasound-guided PENG block provides better early analgesic effects in elderly patients with hip fractures, and a PENG block is safe for elderly patients with hip fractures after hospitalization. Trial registration This study was registered in the Chinese Clinical Trial Testing Center (ID: ChiCTR2200062400).

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.

Deep learning-based automatic license plate recognition methods have made significant advancements and are now widely used in real-world applications. Currently, license plate character recognition primarily relies on classification and the Connectionist Temporal Classification approach. While these methods achieve high recognition accuracy, they face challenges in accurately estimating confidence in occlusion scenarios. To solve this problem, we propose a novel style reconstruction-based network that can transform input license plates into standardized images. It computes character prediction confidence using a lightweight matching module, which effectively reduces the confidence score for occluded sections. Our network ingeniously integrates deep learning with traditional character segmentation methods, offering a fresh perspective on license plate recognition. Besides, to address the class imbalance in existing license plate datasets, we propose a novel synthetic license plate generation method that exchanges styles between real and standardized license plates. In order to comprehensively evaluate license plate recognition models across different regions, we construct and release the Chinese Balanced License Plates (CBLP) dataset, which includes over 30,000 images from all provinces in mainland China. Experimental evaluations on multiple datasets demonstrate that our methods achieve state-of-the-art performance. The code and dataset are available at https://github.com/tj-cvrsg/lpsrnet.