Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments.

Street View Imagery (SVI) has become a valuable geospatial data source for urban analysis, offering rich information about urban environments from a human-centric perspective. However, existing segmentation methods face significant challenges due to the inherent complexities of SVI, including scale variations, occlusions, and diverse semantic hierarchies. Drawing inspiration from the hierarchical nature of human visual cognition, this study proposes the Hierarchical Part-Aware Network (HPAN) to address these challenges in the fine-grained segmentation of SVI. The HPAN framework integrates four key components: (1) a Hierarchical Consistency Learning Module (HCLM), which ensures consistency across different levels of segmentation through novel loss functions; (2) a Topologyaware Graph Matching Module (TGMM), designed to model spatial relationships between object parts; (3) an Edge-guided Feature Enhancement Module (EFEM), which incorporates finegrained edge information; and (4) a Multilevel Joint Attention Module (MLJAM), which adaptively fuses global scene semantics with local object details. Extensive experiments conducted on the Cityscapes Panoptic Parts dataset demonstrate that HPAN outperforms existing methods across multiple panoptic quality metrics, particularly excelling in part-level segmentation tasks. Further evaluations on the Mapillary Vistas dataset and the Cityscapes dataset validate HPAN's robust semantic segmentation performance across diverse street scenes. Generalization tests on different SVI sources, including challenging scenarios such as low-light conditions and occluded environments, highlight the model's strong adaptability and effectiveness

The strapdown inertial navigation system/Doppler velocity log (SINS/DVL)-based localization system provides high positioning accuracy under the bottom-track mode. However, once the DVL loses bottom tracking capability, inertial navigation errors rapidly accumulate. This paper proposes an underwater robot localization method based on ocean current modeling. During the bottom-track mode, an ocean current model is established using the robot’s motion information, including acceleration, velocity relative to water, angular velocity, angular acceleration, attitude, and propulsion force. After bottom-tracking failure, the proposed method estimates the ocean current from the identified model parameters using water-track measurements. The estimated ocean current velocity is used to compensate the DVL-measured velocity relative to water, and the reconstructed ground velocity is used as the measurement vector of the EKF. Measurement updates suppress inertial velocity divergence and improve localization accuracy. Simulation results demonstrate that, without additional hardware, the proposed method significantly improves the accuracy and robustness of SINS/DVL-based localization. Compared with the advanced augmented observable quantity (AOQ) method, the proposed method improves localization accuracy by 55.48%, providing an effective solution for underwater robots operating in complex marine environments.