Momin, Md Abdul and Junos, Mohamad Haniff and Khairuddin, Anis Salwa Mohd and Abu Talip, Mohamad Sofian (2023) Lightweight CNN model: Automated vehicle detection in aerial images. SIGNAL IMAGE AND VIDEO PROCESSING, 17 (4). pp. 1209-1217. ISSN 1863-1703, DOI https://doi.org/10.1007/s11760-022-02328-7.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Industry-Driven Innovation Grant (IDIG) by Universiti Malaya [PPSI-2020-CLUSTER-SD01] |
| Uncontrolled Keywords: | Computer vision; Aerial imagery; Deep neural network; Object detection; Image |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TR Photography |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 10 Jun 2024 03:14 |
| Last Modified: | 10 Jun 2024 03:14 |
| URI: | http://eprints.um.edu.my/id/eprint/39488 |
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