Robust Crosswalk Detection Using Deep Learning Approach

Abstract

The driving assistance system is going to be true shortly. Therefore, developing a robust sub-component system, such as crosswalk detection, is necessary to support the drivers and prevent accidents. Existing crosswalk detection has been studied by using engineering feature-based methods and machine learning-based methods. However, the performance of existing systems based on engineering and machine learning still has several limitations. Therefore, this paper introduces a new approach to improving existing crosswalk detection performance by investigating the benefits of the data augmentation, unsupervised autoencoder, and state-of-the-art YOLO deep learning method. First, the input image is converted to HSV color space, LAB color space, and LUV color space. Second, the HSV, LAB, and LUV color results and raw RBC are then concatenated at the next phase. Thus, the size of the input feature is increased because we join the multiple input features. Therefore, we proposed an approach to use an auto-encoder to reduce the input dimension of the proposed system. The result yields that our proposed crosswalk detection got the detection rate and false alarm rate of 85.45% and 2.5 % under the SKKU dataset, respectively. And, we also got the detection rate and false alarm rate of 88.15% and 1.45 % under our custom fly cam dataset.