ZhuoYi

Introduction

Semantic segmentation using CNN requires large volume and high quality of the training dataset to achieve high performance. These requirements pose challenges to storage and compute hardware resources. However, lower quality dataset induces unwanted artifacts that may destroy the important image information. To solve these challenges, we need to better understand how the quality of training data affects the semantic segmentation algorithm performance. The goal of this project is to see how training data quality affects semantic segmentation network performance.

Methods

In this project, several experiments are conducted to study the connection between the performance of a semantic segmentation algorithm and 2D image/3D lidar point cloud quality. The attributes of 2D images, such as compression ratio and resolution are explored. For 3D lidar data, resolution and data channels are studied.

Compute Hardware

Machine: Macbook Pro

Processor: 2.9 GHz Quad-Core Intel Core i7

Memory: 16 GB 2133MHz LPDDR3

Software

Program: Matlab R2020b

Toolboxes: Deep Learning and Lidar toolboxes

Experiment Data Flow

The following diagram shows the data flow in all the experiments in this project. There are 4 stages. Stage one is data loading the label generation. Data preprocessing and partitioning are in stage two. Network training is in stage three. And then the network evaluation is in the last stage.

Appendix

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