2019/11/29 by Sethu Hareesh Kolluru, Kolluru, Sethu Hareesh
Computer Science · Engineering · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Autonomous Vehicle Technology and Safety #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1911.12993
openalex publication_date 2019/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, the task of pixel-wise semantic segmentation in the context of self-driving with a goal to reduce the inference time is explored. Fully Convolutional Network (FCN-8s, FCN-16s, and FCN-32s) with a VGG16 encoder architecture and skip connections is trained and validated on the Cityscapes dataset. Numerical investigations are carried out for several inference optimization techniques built into TensorFlow and TensorRT to quantify their impact on the inference time and network size. Finally, the trained network is ported on to an embedded platform (Nvidia Jetson TX1) and the inference time, as well as the total energy consumed for inference across hardware platforms, are compared.