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Lightweight CNN model: automated vehicle detection in aerial images

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Abstract

Efficient vehicle detection has played an important role in Intelligent Transportation Systems (ITS) in smart cities. With the development of the Convolutional Neural Network (CNN) for objection detection, new applications have been designed to enable on-road vehicle detection algorithms. Therefore, this work aims to further improve the conventional CNN model for real-time detection on low-cost embedded hardware. In this study, a lightweight CNN model is proposed based on YOLOv4 Tiny to detect vehicles from the VEDAI dataset. In the proposed method, one additional scale feature map is added to make a total of three prediction boxes in the architecture. Then, the output image size of the second and third prediction boxes are upscaled in order to improve detection accuracy in detecting small size vehicles in the aerial images. The proposed model has been evaluated on NVIDIA Geforce 940MX GPU-based computer, Google Collab (TESLA K80) and Jetson Nano. Based on the experimental results, this study has demonstrated that the proposed model achieved better mean average precision (mAP) compared to the conventional YOLOv4 Tiny and previous works.

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Acknowledgements

The research funding is provided by Industry-Driven Innovation Grant (IDIG) by Universiti Malaya with project number PPSI-2020-CLUSTER-SD01.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Md Abdul Momin, Anis Salwa Mohd Khairuddin & Mohamad Sofian Abu Talip

  2. School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Mohamad Haniff Junos

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  1. Md Abdul Momin
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  2. Mohamad Haniff Junos
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  3. Anis Salwa Mohd Khairuddin
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  4. Mohamad Sofian Abu Talip
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Contributions

Md Abdul Momin and Anis Salwa wrote the main manuscript. Mohamad Haniff Junos and Mohamad Sofian Abu Talip prepared the figures and tables. All authors reviewed the manuscript

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Correspondence to Anis Salwa Mohd Khairuddin.

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Momin, M.A., Junos, M.H., Mohd Khairuddin, A.S. et al. Lightweight CNN model: automated vehicle detection in aerial images. SIViP 17, 1209–1217 (2023). https://doi.org/10.1007/s11760-022-02328-7

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  • DOI: https://doi.org/10.1007/s11760-022-02328-7

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