Optimized Convolutional Neural Network for Road Detection with Structured Contour and Spatial Information for Intelligent Vehicle System

"Road detection is said to be a major research area in remote sensing analysis and it is usually complex due to the data complexities as it gets varied in appearance with minor inter-class and huge intra-class variations that often cause errors and gaps in the extraction of the road". Moreover, the majority of supervised learning techniques endure from the high price of manual annotation or inadequate training data. Thereby, this paper intends to introduce a new model for road detection. This work exploits a siamesed fully convolutional network (named as "s-FCN-loc") based on VGG-net architecture that considers semantic contour, RGB channel and location prior for segmenting road regions precisely. As a major contribution, super pixel segmentation was carried out, where the RGB images are given as input to the FCN network and the road regions of images are set as a target. Further, the segmented outputs are fused using AND operation to attain the final segmented output that detects the road regions accurately. To make the detection more accurate, the convolutional layers of FCN are optimally chosen by a new improved model termed as distance oriented sea lion algorithm (DSLnO) model. The presented DSLnO + FCN model has achieved a minimal value of negative measures and accuracy is 8.2% higher than traditional methods. Finally, the presented method is evaluated on the KITTI road detection dataset, and achieves a better result. The analysis was done with respect to positive measures and negative measures.