To address the issues of poor real-time performance and low accuracy in existing subway protection area supervision models, an intelligent inspection system was developed that integrates video surveillance with image recognition and edge computing technologies. Firstly, an edge computing system for protection area inspection was constructed using edge computing terminals and surveillance cameras, effectively reducing reliance on cloud servers. Secondly, the YOLOv8l algorithm was selected as the image recognition model for the intelligent inspection system and deployed on the edge computing terminals. Finally, a B/S architecture -based inspection cloud platform was developed for managing inspection equipment, emergencies, and operations. Through training and testing with approximately 100,000 images of 6 categories of mechanical risk sources under various scenarios, the model achieves a consistent recognition rate exceeding 90% in both daytime and nighttime conditions. Field tests conducted along Guangzhou Metro Line 5 demonstrate an overall system recognition rate of 85.42%, indicating the substantial potential of this video-based intelligent inspection system. This advancement holds significant practical implications for ensuring the sustainable development of urban rail transit systems.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |