基于深度学习卷积神经网络的摩托车检测

Fatin Natasha Ismail, A. Yassin, Adizul Ahmad, M. Ali, R. Baharom
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引用次数: 2

摘要

对于自动驾驶汽车(AV)来说,检测和避开道路上的摩托车非常重要。这是因为在马来西亚发生的大多数事故都涉及摩托车。由于摩托车的低能见度和高速度,检测摩托车是一项具有挑战性的任务。本研究试图利用深度学习神经网络来检测摩托车。培训涉及各种摩托车模型和姿势与不同的决议和道路条件。之所以选择AlexNet网络结构来实现,是因为它在目标检测任务中的性能得到了验证。迁移学习被用来为所描述的任务重新调整AlexNet网络的用途。使用MATLAB深度学习工具箱进行训练和分类。在我们的自定义数据集上的测试结果证明了该方法对任务的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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