| Title |
Monitoring Infrastructure Faults With Yolov5, Assisting Safety Inspectors |
| ID_Doc |
37863 |
| Authors |
Shekhar K.S.; Tanti H.A.; Datta A.; Aggarwal K. |
| Year |
2023 |
| Published |
2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023 |
| DOI |
http://dx.doi.org/10.1109/ICICIS56802.2023.10430270 |
| Abstract |
Recent advances in AI technology paved a wave for real-time computation applications like remote infrastructure inspection. In this paper, we propose preliminary test results of a YOLOv5n-based algorithm diverse enough to visually identify faults in electrical insulators, roads, pavements and concrete, an approach for a single solution for different infrastructural fault inspection in smart cities. This is achieved using a modified algorithm using YOLOv5n, wherein there is a master detection algorithm (MDA) that drives a slave detection algorithm (SDA). The MDA broadly classifies the visual data into different broad categories and transfers the data to the SDAs - fine-tuned algorithms for detecting faults in a single category. Furthermore, using the YOLOv5n made the algorithm lightweight in order to be implemented on an AI device - NVIDIA Jetson Nano. The implemented algorithm resulted in detection time of 90 ms with an overall accuracy of 93 %. © 2023 IEEE. |
| Author Keywords |
AI device; Computer Vision; Near real-time; Object detection; YOLOv5 |
