Real-Time Detection of Vulnerable Road Users Using a Lightweight Object Detection Model

Authors

  • Manish Narkhede Research Center, E&TC Department, Pimpri Chinchwad College of Engineering, Affiliated to Sawitribai Phule Pune University, Pune – 411044, INDIA
  • Nilkanth Chopade Research Center, E&TC Department, Pimpri Chinchwad College of Engineering, Affiliated to Sawitribai Phule Pune University, Pune – 411044, INDIA

Keywords:

Vulnerable Road Users, Object Detection, Deep Learning, Real-Time Processing, Lightweight Model, Road Safety

Abstract

Vulnerable Road Users (VRUs), including pedestrians, cyclists, and motorcyclists, face a heightened risk in traffic scenarios. The safety enhancement for VRUs relies heavily on evolving driver assistance systems and autonomous vehicles, anchored by swift VRU detection and localisation. Object detection models in computer vision are pivotal for this. Deploying such models on edge devices, especially the NVIDIA Jetson Nano presents challenges due to computational and power constraints. Our study compares the SSD MobileNetV2 FPN-Lite 320x320 model on the Jetson Nano for VRU detection with models like YOLOv3 and Faster RCNN. Key findings indicate that the SSD MobileNetV2 FPN-Lite 320x320 model achieves a mean average precision (mAP) of 0.45, precision of 0.80, and recall of 0.65 at 25 FPS in baseline evaluations. With optimisation, the FPS improved to 32 with slight changes in other metrics. The insights also touch upon inherent challenges in VRU detection, suggesting future research directions to refine the SSD MobileNetV2 FPN-Lite model's efficiency, ultimately striving for a safer transportation ecosystem.

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Published

25.12.2023

How to Cite

Narkhede, M. ., & Chopade, N. . (2023). Real-Time Detection of Vulnerable Road Users Using a Lightweight Object Detection Model. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 129–135. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4228

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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