WAN, L.; LI, Z.; ZHANG, C.; CHEN, G.; ZHAO, P.; WU, K. Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots. Preprints2024, 2024080009. https://doi.org/10.20944/preprints202408.0009.v1
APA Style
WAN, L., LI, Z., ZHANG, C., CHEN, G., ZHAO, P., & WU, K. (2024). Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots. Preprints. https://doi.org/10.20944/preprints202408.0009.v1
Chicago/Turabian Style
WAN, L., Panming ZHAO and Kewei WU. 2024 "Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots" Preprints. https://doi.org/10.20944/preprints202408.0009.v1
Abstract
Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in edge computing mobile device using the intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an inprovement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.
Keywords
YOLOv9 + DeepSORT; Edge Computing; Mobile Event Detection; Smart Tunnels
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
