Deep fully convolutional networks with random data augmentation for enhanced generalization in road detection

In this paper, a Deep Learning system for accurate road detection is proposed using the ResNet-101 network with a fully convolutional architecture and multiple upscaling steps for image interpolation. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixelwise changes, such as affine and perspective transformations, mirroring, image cropping, distortions, blur, noise, and color changes. In addition, this paper shows that the use of a 4-step upscaling strategy provides optimal learning results as compared to other similar techniques that perform data upscaling based on shallow layers with scarce representation of the scene data. The complete system is trained and tested on data from the KITTI benchmark and besides it is also tested on images recorded on the Campus of the University of Alcala (Spain). The improvement attained after performing data augmentation and conducting a number of training variants is really encouraging, showing the path to follow for enhanced learning generalization of road detection systems with a view to real deployment in self-driving cars.