An Evaluation of a CNN-Based Parking Detection System with Webcams
An Evaluation of a CNN-Based Parking Detection System with Webcams
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发表时间：
2020-12
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通讯作者：
Takuto Fukusaki;Hiroshi Tsutsui;T. Ohgane
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作者：
Takuto Fukusaki;Hiroshi Tsutsui;T. Ohgane
在本文中，我们评估了基于图像处理的停车检测系统，利用卷积神经网络（CNN）。目前，对室外停车场的使用调查通常是人工进行的，这可能会花费很多。通过使用商用网络摄像头和图像处理，可以以相当低的成本部署停车检测系统。一些停车检测方法利用HOG和SIFT特征值以及RGB和HSV值的时间变化。然而，由于环境光的影响，这些方法具有困难。为了解决这个问题，我们提出了一种利用CNN的停车检测方法，CNN在分类和对象识别应用中具有很高的潜力。通过用不同的环境光和照明条件训练CNN，预计所提出的方法可以克服与环境光变化相关的问题。我们评估建议的停车检测系统的准确性相比，没有机器学习的方法，即基于颜色的方法。实验结果表明，该方法可以达到99%的准确率停车和空置检测，导致F值为0.996。
In this paper, we evaluate an image processing based parking detection system utilizing convolutional neural networks (CNNs). At present, usage surveys on outdoor parking lots are often performed manually, which may cost a lot. By using commodity webcams and image processing, it may be possible to deploy a parking detection system at a quite low cost. Some parking detection methods utilize HOG and SIFT feature values, and temporal changes of RGB and HSV values. However, these approaches have difficulties due to the influence of ambient light. To tackle this issue, we propose a parking detection method utilizing CNNs, which have high potential in classification and object recognition applications. By training CNNs with different ambient light and lighting conditions, it is expected that the proposed approach can overcome the issue related to the ambient light changes. We evaluate the accuracy of the proposed parking detection system comparing with a method without machine learning, that is, a color-based approach. Experimental results show that the proposed approach can achieve 99 % accuracy for parking and vacancy detection, resulting in an F value of 0.996.