| dc.description.abstract | In steel structure engineering, using bolts to connect steel components is a common method that ensures a secure connection between structural elements. However, due to natural or human factors such as climate, external forces, and earthquakes, bolts may experience defects such as loosening or detachment, leading to a decrease in the safety of steel structures. Currently, bolt inspection work is mostly performed by inspectors manually checking for loosening by using rubber hammers and other tools. These inspection tasks are labor-intensive, time-consuming, and may involve safety risks or difficulties in inspection.
To address this, this study combines the concept of Tiny Machine Learning with microcontrollers to develop a lightweight real-time multi-target bolt defect image detection system for climbing inspection robots. This system is used to detect the defect patterns of bolts, which are categorized into ＂Normal,＂ ＂Loosen,＂ and ＂Miss.＂ The Faster Objects, More Objects (FOMO) algorithm is used for the visual detection task, and the trained model is deployed in the visual detection module, combined with a magnetic climbing robot for actual bolt inspection tests.
The trained model achieved an F1 score of 74.8% on the validation set and an F1 score of 72.9% on the test set, while the quantized model deployed on the microcontroller achieved an F1 score of 72.4% on the validation set. In actual outdoor box beam tests, the best-case achieved precision and recall rates of 89% and 82%, respectively, while the worst-case achieved 57% and 67%. The average precision and recall rates for all cases were 77% and 76%, respectively.
The defect detection system can provide real-time feedback on the bolt recognition results, allowing inspectors to remotely control the device for inspection tasks. The entire system is lightweight, low-energy consuming, and can be configured on different carriers for inspection tasks. In the future, bolt defect detection is expected to become easier and reduce manpower requirements. The overall instrument cost for inspection work can be reduced, and the goals of lightweight and high-speed detection are pursued in future bolt inspection tasks. | en_US |