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LV-YOLO: logistic vehicle speed detection and counting using deep learning based YOLO network

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Abstract

In the era of smart cities and advancing transportation technologies, predicting logistic vehicle and vehicle speed is pivotal to enhancing traffic management, safety, and overall transportation efficiency. Properly predicting vehicle and vehicle speed is critical to the interests of both road users and traffic authorities. However, accurately predicting the vehicle speed and logistics vehicle of a single trip is a difficult task. In some cases, unpredicted accidents will happen, so death cases will increase. To overcome these issues, a novel Logistic Vehicle speed detection using the YOLO (LV-YOLO) method has been introduced to detect logistical vehicles and speed using the YOLO network. The proposed framework is divided into three layers such as image acquisition, segmentation layer, and detection layer. In the image acquisition layer, a CCTV camera captures highway traffic video. The collected video is converted into frames. In the segmentation layer, the video frame is segmented using U-Net, which segments the vehicle in the video frames. The detection layer performs truck detection, and speed detection using LV-YOLO on segmented frames based on the Boxy Vehicle dataset. The simulated results show that the LV-YOLO technique maintains excellent mAP levels of 99.42%. The LV-YOLO improves the overall mAP by 1.72, 5.42, and 0.82% better than the Simple Vehicle Counting System, Real-Time Detection, and Advance YOLOv3 Model for vehicle detection, 4.81, and 2.63% better than Deep Learning and CAN protocol, and 1D-CNN speed estimation mode for speed prediction respectively.

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Acknowledgements

The authors would like to thank the reviewers for all of their careful, constructive and insightful comments in relation to this work.

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The authors confirm contribution to the paper as follows: Study conception and design: Gopika rani N, Hema priya N, Ahilan A, Muthukumaran N; Data collection: Gopika rani N, Hema priya. N, Ahilan A, Muthukumaran N; Analysis and interpretation of results: Gopika rani N, Hema priya N, Ahilan A, Muthukumaran N; Draft manuscript preparation: Gopika rani, Hema priya N, Ahilan A, Muthukumaran N; All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to N. Gopika Rani.

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Rani, N.G., Priya, N.H., Ahilan, A. et al. LV-YOLO: logistic vehicle speed detection and counting using deep learning based YOLO network. SIViP 18, 7419–7429 (2024). https://doi.org/10.1007/s11760-024-03404-w

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