Andika, Johan Lela and Khairuddin, Anis Salwa Mohd and Ramiah, Harikrishnan and Kanesan, Jeevan (2024) Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware. Journal of Real-Time Image Processing, 21 (3). p. 77. ISSN 1861-8200, DOI https://doi.org/10.1007/s11554-024-01457-1.
Full text not available from this repository.Abstract
The advancement of unmanned aerial vehicles (UAVs) has drawn researchers to update object detection algorithms for better accuracy and computation performance. Previous works applying deep learning models for object detection applications required high graphics processing unit (GPU) computation power. Generally, object detection models suffer trade-off between accuracy and model size where the relationship is not always linear in deep learning models. Various factors such as architectural design, optimization techniques, and dataset characteristics can significantly influence the accuracy, model size, and computation cost in adopting object detection models for low-cost embedded devices. Hence, it is crucial to employ lightweight object detection models for real-time object identification for the solution to be sustainable. In this work, an improved feature extraction network is proposed by incorporating an efficient long-range aggregation network for vehicle detection (ELAN-VD) in the backbone layer. The architecture improvement in YOLOv7-tiny model is proposed to improve the accuracy of detecting small vehicles in the aerial image. Besides that, the image size output of the second and third prediction boxes is upscaled for better performance. This study showed that the proposed method yields a mean average precision (mAP) of 57.94%, which is higher than that of the conventional YOLOv7-tiny. In addition, the proposed model showed significant performance when compared to previous works, making it viable for application in low-cost embedded devices.
| Item Type: | Article |
|---|---|
| Funders: | Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya (PV007-2023) ; (PV013-2023) ; (MG006-2023) |
| Uncontrolled Keywords: | Image; Object detection; Vehicle detection; YOLO |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Sep 2024 05:04 |
| Last Modified: | 30 Sep 2024 05:04 |
| URI: | http://eprints.um.edu.my/id/eprint/45256 |
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