The determination of locations and sizes for such a system is important in a drainage master plan or a storm-water management system. However, the distribution of detentions in the upstream and midstream is often more dispersed using many combinations of volume scales. This paper uses the non-dominated sorting genetic algorithm combined with the Storm Water Management Model to explore and calculate the optimal layout scheme for decentralized rainwater detention. The purpose is to find a design and planning method that can achieve the optimal balance of decentralized detention considering the aspects of flood disaster control, peak flow reduction, and investment cost. The optimal results of Pareto in applied case show that among the five most unfavourable nodes, the detentions with different layout volumes and relatively smaller size can control water logging from rainstorm. The project cost is effectively reduced and the standard of the return period of the regional rainwater system is enhanced from 2 to 20 years.

No abstract

No abstract

Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.

Urban rail transit, especially the subway, has been booming in China for a decade, imposing safety challenges on all related parties. Drivers' behaviours are particularly crucial. Typically, drivers' actions are recorded by cameras, and the surveillance videos are evaluated manually. Current driver behaviour recognition methods mostly target the bus or car drivers and can hardly be implemented for subways, because subway drivers follow a rigid working code that needs a time sequence of movements to describe. In this study, we propose a recognition model to automatically recognise behaviours from single-frame images that are extracted from surveillance videos; second, we convert the recognition results into time series diagrams, thus the recognised behaviours can be interpreted and analysed statistically and effectively. The validation experiments demonstrate that the convolutional neural network model can recognise 96.20% driver behaviours, and time series diagrams add time information to the behaviours, providing a convincible reference for subway driver evaluation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.