A Deep Neural Network Architecture for Real-Time Semantic Segmentation on Embedded Board

Junyeop Lee, Youngwan Lee

http://doi.org/10.5626/JOK.2018.45.1.94

We propose Wide Inception ResNet (WIR Net) an optimized neural network architecture as a real-time semantic segmentation method for autonomous driving. The neural network architecture consists of an encoder that extracts features by applying a residual connection and inception module, and a decoder that increases the resolution by using transposed convolution and a low layer feature map. We also improved the performance by applying an ELU activation function and optimized the neural network by reducing the number of layers and increasing the number of filters. The performance evaluations used an NVIDIA Geforce GTX 1080 and TX1 boards to assess the class and category IoU for cityscapes data in the driving environment. The experimental results show that the accuracy of class IoU 53.4, category IoU 81.8 and the execution speed of 640x360, 720x480 resolution image processing 17.8fps and 13.0fps on TX1 board.