<p>In
this project, CNN has been applied as a training tool to process image
classification and object avoidance on remote robotic cars built with the Nvidia Jetson Nano developer kit. The kit was programmed using the wireless programming
environment, Jupyter notebook. In addition, two different CNN models have been applied to
analyze the output result performance. The main purpose is to
train the robot to identify objects and improve its accuracy. The recognition
and accuracy rate under different conditions can be observed by comparing the
two models with different graphic inputs conditions. This project adopts the
pre-train model for real time
demonstrations and can be executed in a cloudless environment (without networks
involved). As a result, the robot can achieve a high accuracy rate in both CNN
models output result performance. Moreover, the pre train model can execute in
local service to accomplish cloudless.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12722381 |
| Date | 27 July 2020 |
| Creators | Chih Yung Tseng (9174176) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Path_finding_of_auto_driving_car_using_deep_learning/12722381 |
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