Lightweight real-time vehicle detection algorithm on edge computing platform

Xingzhen Xu; Qiangye Gao; Qifeng Zhou; Yating Chu; Yongdong Xie

doi:10.1117/12.3053321

3 January 2025 Lightweight real-time vehicle detection algorithm on edge computing platform

Xingzhen Xu, Qiangye Gao, Qifeng Zhou, Yating Chu, Yongdong Xie

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134422B (2025) https://doi.org/10.1117/12.3053321
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

To address these issues, this study proposes a lightweight and highly deployable vehicle detection model. The model builds upon the single-stage YOLOv7 architecture, replacing the original convolutional layers with the inverted residual structure and depth-wise separable convolutions from MobileNetv3. This streamlines the network width and computational parameters. Furthermore, an ECA attention mechanism is incorporated into both the backbone network and multi-scale feature branches to reduce computational overhead while enhancing the model's cross-channel feature extraction capabilities. Additionally, data augmentation techniques were applied to the vehicle dataset, and the Focal Loss strategy was employed for model training and evaluation. Experimental results demonstrate that the proposed model reduces the parameter count from 37.62M to 10.217M, achieving a competitive MAP of 77.59%. The proposed model achieves a real-time inference speed of 40.04 FPS when deployed on a Jetson Xavier NX edge platform, representing a 77.8% performance improvement over the original YOLOv7 model. The lightweight and high-performance characteristics of the proposed detection model enable its seamless integration into resource-constrained edge computing infrastructure for ITS applications. This work provides a valuable technical foundation for realizing the full potential of intelligent.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingzhen Xu, Qiangye Gao, Qifeng Zhou, Yating Chu, and Yongdong Xie "Lightweight real-time vehicle detection algorithm on edge computing platform", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134422B (3 January 2025); https://doi.org/10.1117/12.3053321

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KEYWORDS

Object detection

Data modeling

Target detection

Detection and tracking algorithms

Deep learning

Edge detection

Real-time computing

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