Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm DOI Creative Commons
B. Zhang, Zhuqi Li, Bingjie Li

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(11), P. 711 - 711

Published: Nov. 19, 2024

Despite the implementation of numerous interventions to enhance urban traffic safety, estimation risk crashes resulting in life-threatening and economic costs remains a significant challenge. In light above, an online inference method for crash based on self-developed TAR-DETR WOA-SA-SVM methods is proposed. The method's robust data capabilities can be applied autonomous mobile robots vehicle systems, enabling real-time road condition prediction, continuous monitoring, timely roadside assistance. First, dataset object detection, named TAR-1, created by extracting information from major roads around Hainan University China incorporating Russian car news. Secondly, we develop innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). model demonstrates detection accuracy 76.8% objects, which exceeds performance other state-of-the-art models. employed TAR-1 extract features, feature was designated as TAR-2. TAR-2 comprises six features three categories. A new algorithm proposed optimize parameters (C, g) SVM, thereby enhancing robustness inference. developed combining Whale Optimization Algorithm (WOA) Simulated Annealing (SA), Hybrid Bionic Intelligent Algorithm. inputted into Support Vector Machine (SVM) optimized using hybrid used infer crashes. achieves average 80%

Language: Английский

An Intelligent Monitoring System for the Driving Environment of Explosives Transport Vehicles Based on Consumer-Grade Cameras DOI Creative Commons
Jinshan Sun, Jianchuan Tang,

Ronghuan Zheng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 4072 - 4072

Published: April 7, 2025

With the development of industry and society, explosives are widely used in social production as an important industrial product require transportation. Explosives transport vehicles susceptible to various objective factors during driving, increasing risk At present, new generally equipped with intelligent driving monitoring systems. However, for old vehicles, cost installing such systems is relatively high. To enhance safety older this study proposes a cost-effective system using consumer-grade IP cameras edge computing. The integrates YOLOv8 real-time vehicle detection novel hybrid ranging strategy combining monocular (fast) binocular (accurate) techniques measure distances, ensuring rapid warnings precise proximity monitoring. An optimized stereo matching workflow reduces processing latency by 23.5%, enabling performance on low-cost devices. Experimental results confirm that meets requirements, offering practical, application-specific solution improving resource-limited explosive environments.

Language: Английский

Citations

0
Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm DOI Creative Commons
B. Zhang, Zhuqi Li, Bingjie Li

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(11), P. 711 - 711

Published: Nov. 19, 2024

Despite the implementation of numerous interventions to enhance urban traffic safety, estimation risk crashes resulting in life-threatening and economic costs remains a significant challenge. In light above, an online inference method for crash based on self-developed TAR-DETR WOA-SA-SVM methods is proposed. The method's robust data capabilities can be applied autonomous mobile robots vehicle systems, enabling real-time road condition prediction, continuous monitoring, timely roadside assistance. First, dataset object detection, named TAR-1, created by extracting information from major roads around Hainan University China incorporating Russian car news. Secondly, we develop innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). model demonstrates detection accuracy 76.8% objects, which exceeds performance other state-of-the-art models. employed TAR-1 extract features, feature was designated as TAR-2. TAR-2 comprises six features three categories. A new algorithm proposed optimize parameters (C, g) SVM, thereby enhancing robustness inference. developed combining Whale Optimization Algorithm (WOA) Simulated Annealing (SA), Hybrid Bionic Intelligent Algorithm. inputted into Support Vector Machine (SVM) optimized using hybrid used infer crashes. achieves average 80%

Language: Английский

Citations

0