Full
Outline
To address the challenges of low efficiency, high miss rate, and insufficient imaging stability associated with manual visual inspection in traditional automotive Body-In-White (BIW) joining processes, an intelligent inspection system based on computer vision and deep learning technologies is developed. An industrial camera imaging system is constructed with optimized optical parameters, and image preprocessing is realized using automatic masking techniques based on interactive image segmentation. Object detection is performed by incorporating an end-to-end object detection model (DETR). A full-stack data management architecture encompassing data acquisition, processing, storage, and visualization is established. Experimental results demonstrate that the system achieves a detection accuracy of over 99%, reduces task completion time by 80%, and lowers the miss rate by 67%. Both inspection efficiency and result consistency are significantly improved, satisfying the requirements for batch inspection of BIWs.
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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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