A Hybrid Lane Detection Model for Wild Road Conditions

Abstract

This paper presents a lane detection system in unstructured, complex environments in real time. We formulated lane detection as a binary segmentation problem and proposed a novel hybrid Convolutional Neural Network (CNN) architecture by fusing encoder-decoder network with a sequence of dilated convolution network. We computed weighted average of outputs of these branches for lane detection and introduced a new loss function for binary semantic segmentation to counter the imbalance between lane and non-lane pixels. The proposed model achieved 95.19% accuracy on TuSimple dataset. To evaluate the system with respect to unstructured road scenarios, we created an Indian Lane Dataset (ILD) with 6149 labelled images from India Driving Dataset (IDD). We reported mIoU (Intersection Over Union) of 0.31 on IDD which was higher than other state-of-the art lane detection models. Finally, we undertook an ablation study to understand effectiveness of two parallel branches, i.e., encoder-decoder and dilated convolution branches and the proposed loss function.