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Iranian Journal of Electrical and Electronic Engineering، جلد ۲۱، شماره ۲، صفحات ۳۵۹۶-۳۵۹۶
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عنوان انگلیسی Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery
چکیده انگلیسی مقاله Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. One-stage detection models are currently state-of-the-art in object detection due to their faster detection times. However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. To solve these problems, this work proposes an enhanced lightweight object detection model based on the YOLOv4 Tiny model. The proposed model incorporates multiple modifications, including integrating a Mix-efficient layer aggregation network within its backbone network to optimize efficiency by reducing parameter generation. Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. These findings demonstrate that the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time, making it highly suitable for deployment on embedded devices with limited capacity.
کلیدواژه‌های انگلیسی مقاله Lightweight architecture, Modified YOLO, Satellite Image, Vehicle Detection.
نویسندگان مقاله | Mohamad Haniff Junos
School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.


| Anis Salwa Mohd Khairuddin
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.


| Elmi Abu Bakar
School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.


| Ahmad Faizul Hawary
School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5471-2&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Machine Learning
نوع مقاله منتشر شده Only For Articles of ELECRiS 2024
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