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To improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work, reduce the probability of traffic accidents. A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed. This method incorporated the ASFF module into the original YOLOv5s network, replaced the backbone network with the FasterNet network, and further introduced the Efficient Channel Attention (ECA) module. Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model, to verify the target detection effect, and to develop an interactive visualized detection interface. The results show that the improved model accuracy, recall rate, and average detection accuracy have increased by 4.1%, 9.9% and 5.6% respectively. Compared to the original network, the improvement is significant. It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves, thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.
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| 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 |
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