Motorcycle detection using deep learning convolution neural network
Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on De...
| Published in: | 2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098244060&doi=10.1109%2fICSET51301.2020.9265361&partnerID=40&md5=4dc1834877874e79dfc0ac0a1024c47f |
| Summary: | Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on Deep Learning Neural Network to detect motorcycles. Training involves various motorcycle models and poses with different resolutions and road conditions. The AlexNet network structure was chosen for implementation due to its proven performance in object detection tasks. Transfer learning was used to repurpose the AlexNet network for the described task. Training and classification were performed using the MATLAB Deep Learning Toolbox. Test results on our custom dataset demonstrates the effectiveness of the approach for the task. © 2020 IEEE |
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| DOI: | 10.1109/ICSET51301.2020.9265361 |