ADFNet: Accumulated decoder features for real-time semantic segmentation

Abstract

Semantic segmentation is one of the important technologies in autonomous driving, and ensuring its real-time and high performance is of utmost importance for the safety of pedestrians and passengers. To improve its performance using deep neural networks that operate in real-time, the authors propose a simple and efficient method called ADFNet using accumulated decoder features, ADFNet operates by only using the decoder information without skip connections between the encoder and decoder. They demonstrate that the performance of ADFNet is superior to that of the state-of-the-art methods, including that of the baseline network on the cityscapes dataset. Further, they analyse the results obtained via ADFNet using class activation maps and RGB representations for image segmentation results. © The Institution of Engineering and Technology 2020.