Road Curb Detection Based on a Deep Learning Framework

Detecting curbs efficiently and at a low cost is essential for autonomous driving. Most of the existing methods rely on light detection and ranging, which is a complicated and costly solution. Therefore, this work propose an efficient method for detecting curbs in a single road image. This method utilizes the convolutional neural network (CNN) framework to localize the curb parts in the captured image. We built a feature-extraction CNN that can classify curb and no-curb regions. The relu, pooling, fully connected, softmax, and classification layers in the pretrained ResNet18 are replaced with ten new layers, which are relu, convolution2d, batchnormalization, dropout, fully connected, relu, maxpooling2d, fully connected, softmax, and classification layers. The comparison of several evaluation metrics to prove the effectiveness of the customized network structure for curb classification. The customized network structure is assembled in the Faster R-CNN detector to improve the performance of feature extraction both for classification and detection. In the detection stage, although the layer we used for feature extraction was the previous layer of the added layers, the detection experimental results demonstrate that our custom CNN network structure can improve the classification performance of the feature extraction layer.