Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

This study presents a shallow and robust road segmentation model. The computer-aided real-time applications, like driver assistance, require real-time and accurate processing. Current studies use Deep Convolutional Neural Networks (DCNN) for road segmentation. However, DCNN requires high computational power and lots of labeled data to learn abstract features for deeper layers. The deeper the layer is, the more abstract information it tends to learn. Moreover, the prediction time of the DCNN network is an important aspect of autonomous vehicles. To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations of the input image. Gradient information of the input image is used as additional channels to enhance the learning process of the proposed deep learning architecture. The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions. The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as compared to baseline CNN consisting of only RGB inputs. Furthermore, the comparison of the proposed method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme performs better while being more efficient during training and evaluation. Unlike traditional segmentation techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only the encoder network. The proposed MVS-CNN has been trained and validated with two well-known datasets: the KITTI Vision Benchmark and the Cityscapes dataset. The results have been compared with the state-of-the-art deep learning architectures. The proposed MVS-CNN outperforms and shows supremacy in terms of model accuracy, processing time, and segmentation accuracy. Based on the experimental results, the proposed architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.