Curved Road Lanes Detection Using Fully Convolutional Neural Network

The ever-growing expansion of highway transportation infrastructure leads to significant role in development of every nation. The number of people owning four-wheeler vehicle has increased every year with automatic driving cars attracts people, further it will make a great impact in short to long distance travel. The autonomous vehicles generally use lane and curve detection approaches to automatically discover the way using deep learning algorithms. To tackle the problem of inadequate robustness for extracting multi-lane marking, a multi-lane identification strategy relying on a deep neural network based on FCN network is proposed in this paper. The ResNet-152 based fully convolutional network is used for lane and curve detection. The ResNet-152 model is chosen in this work because the existing models viz. VGG-16, AlexNet performs worse when number of layers increased, further error rate also get increased, but in ResNet-152 error rate decreases with increasing layers. The ResNet-152 model used skip connections. Finally, the fit interval is determined using the Hough Transform, and the lane marker is fitted using the least square method. The experiments are carried out on the TuSimple dataset. The suggested FCN-based lane and curve detection approach performs well on the detection of curved lane roads.