Bolts on rail transit evacuation platforms are often concealed, making comprehensive manual inspection difficult and increasing the risk of loosening or detachment that could compromise operational safety. This paper proposes an intelligent inspection system integrating machine vision and laser scanning to achieve precise detection of bolt defects and clearance-intrusion trends. The system employs a dual-track autonomous robot equipped with a high-resolution camera and LiDAR, enabling comprehensive inspection through image recognition and point-cloud processing. First, high-resolution image acquisition combined with Adaptive Contrast Enhancement (ACE) is used to improve image quality. Optimized YOLOv11 and Mask R-CNN models are then applied for small-object detection and segmentation, enabling automatic identification of missing bolts, loose nuts, and insufficient bolt protrusion, with an accuracy of 94.4%. Furthermore, laser point-cloud modeling combined with a vector cross-product algorithm is employed to provide clearance-intrusion warnings with a precision of 2 cm. Field tests conducted on the Hangzhou-Haining Intercity Railway, across both elevated and tunnel sections, demonstrate an F1-score of 92.6% for bolt defect recognition and a 0% false alarm rate for clearance detection. The proposed system significantly enhances inspection efficiency and provides technical support for intelligent maintenance of rail transit infrastructure.
| 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 |