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# OSM-Crosswalk-Detection: Deep learning based image recognition

## Introduction

OSM-Crosswalk-Detection is a highly scalable image recognition software for aerial photos (orthophotos). It uses the deep learning library Keras, more precisely a VGGNet like convolutional neural network, to detect crosswalks along streets.
This Python based project provides a Keras docker container to train the neural network on a GPU and several docker containers to distribute the recognition work on multiple servers.

This work started as part of a semester thesis autumn 2015 at Geometa Lab, University of Applied Sciences Rapperswil (HSR).

## Overview

![Marcitecture](http://s11.postimg.org/7bdx1cetf/SA_Overview_new.png)

## Examples
![Detection-Example1](imgs/preview_crosswalk_rappi2.png)

Picture 1: Rapperswil trainstation (47.226468, 8.818477)

![Detection-Example2](imgs/preview_crosswalk_rappi.png)<commit_msg>changed readme introduction<commit_after>[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
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# OSM-Crosswalk-Detection: Deep learning based image recognition

## Introduction

OSM-Crosswalk-Detection is a highly scalable image recognition software for aerial photos (orthophotos). It uses the open source software library TensorFlow, with a retrained Inception V3 neuronal network, to detect crosswalks along streets.

This work started as part of a semester thesis autumn 2015 at Geometa Lab, University of Applied Sciences Rapperswil (HSR).

## Overview

![Marcitecture](http://s11.postimg.org/7bdx1cetf/SA_Overview_new.png)

## Examples
![Detection-Example1](imgs/preview_crosswalk_rappi2.png)

Picture 1: Rapperswil trainstation (47.226468, 8.818477)

![Detection-Example2](imgs/preview_crosswalk_rappi.png)
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