CNN-based Fisheye Image Real-Time Semantic Segmentation

By Álvaro Sáez, Luis Miguel Bergasa, Eduardo Romera, M. E. L. Guillén, R. Barea, Rafael Sanz. 2018

35 citations recorded by the source corpus.

Semantic segmentation based on Convolutional Neural Networks (CNNs) has been proven as an efficient way of facing scene understanding for autonomous driving applications. Traditionally, environment information is acquired using narrow-angle pin-hole cameras, but autonomous vehicles need wider field of view to perceive the complex surrounding, especially in urban traffic scenes. Fisheye cameras have begun to play an increasingly role to cover this need. This paper presents a real-time CNN-based semantic segmentation solution for urban traffic images using fisheye cameras. We adapt our Efficient Residual Factorized CNN (ERFNet) architecture to handle distorted fish-eye images. A new fisheye image dataset for semantic segmentation from the existing CityScapes dataset is generated to train and evaluate our CNN. We also test a data augmentation suggestion for fisheye image proposed in [1]. Experiments show outstanding results of our proposal regarding other methods of the state of the art.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

Citations in the corpus

• WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, neutral): “Semantic segmentation networks for autonomous driving [47] have been successfully trained directly on fisheye images in [12, 45].”