Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots
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 ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/11/147 |
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| _version_ | 1846154307995435008 |
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| author | Li Wan Zhenjiang Li Changan Zhang Guangyong Chen Panming Zhao Kewei Wu |
| author_facet | Li Wan Zhenjiang Li Changan Zhang Guangyong Chen Panming Zhao Kewei Wu |
| author_sort | Li Wan |
| collection | DOAJ |
| description | 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 an edge computing mobile device using an 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 improvement 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. |
| format | Article |
| id | doaj-art-35388a7beca44637bc03fb71c3e7c55a |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-35388a7beca44637bc03fb71c3e7c55a2024-11-26T17:51:10ZengMDPI AGBig Data and Cognitive Computing2504-22892024-10-0181114710.3390/bdcc8110147Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel RobotsLi Wan0Zhenjiang Li1Changan Zhang2Guangyong Chen3Panming Zhao4Kewei Wu5Shandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaShandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaShandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaShandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, ChinaBeijing Zhuoshi Zhitong Technology Co., Ltd., Beijing 100080, ChinaBeijing Zhuoshi Zhitong Technology Co., Ltd., Beijing 100080, ChinaMobile 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 an edge computing mobile device using an 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 improvement 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.https://www.mdpi.com/2504-2289/8/11/147YOLOv9+DeepSORTedge computingmobile event detectionsmart tunnels |
| spellingShingle | Li Wan Zhenjiang Li Changan Zhang Guangyong Chen Panming Zhao Kewei Wu Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots Big Data and Cognitive Computing YOLOv9+DeepSORT edge computing mobile event detection smart tunnels |
| title | Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots |
| title_full | Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots |
| title_fullStr | Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots |
| title_full_unstemmed | Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots |
| title_short | Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots |
| title_sort | algorithm improvement for mobile event detection with intelligent tunnel robots |
| topic | YOLOv9+DeepSORT edge computing mobile event detection smart tunnels |
| url | https://www.mdpi.com/2504-2289/8/11/147 |
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