Full
Outline
The anchoring operation of coal mine excavation roadway is the key factor affecting the tunneling efficiency. Accurately determining the position of anchor hole under the condition of roadway with many interference factors is one of the key links to improve the anchoring speed. At present, most of the research on anchor hole positioning has a single consideration of interference factors, and the influence of complex factors on anchor hole identification under multi-scale changes has not been studied. To this, an improved YOLOv8s anchor hole positioning method in multi-scale complex environment was proposed. Firstly, according to the actual anchoring situation of the roadway, four main factors affecting the positioning of the anchor hole, such as light intensity, dust concentration, water mist concentration and shooting distance, were summarized, and a three-level influence scale was established. The initial data set was formed by the combination of various influencing factors. Before the anchor hole identification, the image preprocessing operation was performed to optimize the overall quality of the image. Then, the YOLOv8s deep learning network model was improved, and the multi-scale hole attention mechanism (MSDA) was added to improve the feature extraction ability of the model under complex conditions. Finally, the pixel coordinates of the anchor hole center were calculated to obtain the actual three-dimensional coordinates. The results show that the average detection precision of the improved YOLOv8s model reaches 91.6%, which is 5.3 percentage points higher than that of the YOLOv8s model. The improved YOLOv8s model has better detection ability and can accurately detect the anchor hole position in the image under the interference of different scale combination influencing factors. The number of frames per second of the processed image of the improved YOLOv8s model is maintained at 171 frames, which can meet the real-time requirements of the model detection function.
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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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