Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision ($82.97\%$) and recall ($79.39\%$). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets.

Ship License Plate Recognition (SLPR) technology serves as a fundamental technological foundation for maritime transportation management. Automated ship identification enhances both regulatory oversight and operational efficiency. However, current recognition models demonstrate significant limitations, including their inability to detect objects in complex environments and challenges in maintaining real-time performance while ensuring accuracy, thereby limiting their practical applicability. This study proposes a novel cascaded framework that integrates RT-DETR-based detection with OCR capabilities. The framework incorporates several key methodological innovations: optimizing the RT-DETR backbone through efficient partial convolutions during training to improve computational efficiency; implementing Conv3XC to modify the ResNet18-backbone BasicBlock using a triple convolutional layer configuration with an enhanced RepC3 kernel design for better feature extraction; and integrating learned position encoding (LPE) to improve the AIFI position encoding mechanism, thereby enhancing detection capabilities. After region detection, PP-OCRv3 is used for character recognition. Experimental results demonstrate the superior performance of our approach: Ship-RT-DETR achieves 96.2% detection accuracy with a 28.5% reduction in parameters and 67.3 FPS, while PP-OCRv3 achieves 91.6% recognition accuracy. Extensive environmental validation across diverse weather conditions (sunny, cloudy, rainy, and foggy) confirms the framework’s robustness, maintaining a detection accuracy above 90% even in challenging foggy conditions, with minimal performance degradation (a 7.7% decrease from optimal conditions). The system’s consistent performance across various environmental conditions (detection standard deviation: 2.84%, OCR confidence standard deviation: 0.0295) establishes a novel and robust methodology for practical SLPR applications.

The MASSIVE dataset is a spoken-language comprehension resource package for slot filling, intent classification, and virtual assistant evaluation tasks. It contains multi-language utterances from human beings communicating with a virtual assistant. In this paper, we exploited the relationship between intent classification and slot filling to improve the exact match accuracy by proposing five models with hierarchical and bidirectional architectures. There are two variants for hierarchical architectures and three variants for bidirectional architectures. These are the hierarchical concatenation model, the hierarchical attention-based model, the bidirectional max-pooling model, the bidirectional LSTM model, and the bidirectional attention-based model. The results of our models showed a significant improvement in the averaged exact match accuracy. The hierarchical attention-based model improved the accuracy by 1.01 points for the full training dataset. As for the zero-shot setup, we observed that the exact match accuracy increased from 53.43 to 53.91. In this study, we observed that, for multi-task problems, utilizing the relevance between different tasks can help in improving the model’s overall performance.

Retinal swelling caused by fundus fluids is one of the most sight-threatening retinal abnormalities, which may lead to irreversible blindness. Common retinal diseases, such as diabetic macular edema (DME), can cause multiple fundus fluids, including intraretinal fluid (IRF) and subretinal fluid (SRF). Hence, highly accurate and automatic diagnosis systems are vital for modern digital healthcare centers and the progress of ubiquitous intelligence. Although extensive efforts have been made to develop deep-learning-based automatic diagnosis systems under the modal of optical coherence tomography (OCT), complex retina anatomies and fine-grained fundus fluid spatial features still significantly challenge the existing methods’ clinical applications. Therefore, we proposed a novel Anatomy-Aware Probe Attention Network (A2PAN) for OCT multi-label fundus fluid categorization. Specifically, we designed an Anatomy-Aware Probe Attention (A2PA) module to probe and capture multi-scale anatomy features. Moreover, we proposed an Anatomy Attention Consistency Loss (A2C-Loss) to promote modeling retinal anatomy structures and spur the recognition of multiple fundus fluids in a simple yet effective paradigm. Extensive quantitative results and qualitative analysis of our dataset demonstrate the effectiveness of our A2PAN and its related clinical prospect. Furthermore, our A2PAN outperforms several state-of-the-art networks dedicated to multi-label medical image categorization on a publicly available fluid dataset. The code will be available at: https://github.com/wpppc/A2PAN.