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Advanced lane detection based on car driver assistance systems using opencv with implementation of vehicle and pedestrian detection

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The goal of this thesis project is to be able to provide a reliable safety driver assistance system for drivers by detection of road lane boundaries where the source of the vision is from a camera which is mounted inside the car in order to get the vision since the system is a vision based system, the object detection should be able to classify whether it is a vehicle or a pedestrian with Deep learning method where the data is trained using TensorFlow. The lane detection and object detection system were constructed in a C++ language with Qt as the framework and the integration of both systems is using multithread. Deep learning is applied based on the machine learning approach it is selected as the method used in this research. By implementing lane detection and object detection, it enables the detection of position of the lane boundaries and the surrounding objects in the environment would be detected. Classification of object and lane detection system was tested using a data video recorded in Indonesia and developed using a dashcam/webcam as the vision.

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Availability

B02953

(Rack Thesis)

Available

Detail Information

Series Title

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Call Number

2953

Publisher

: Swiss German University., 2019

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

MT
TensorFlow
Deep Learning
Object detection
Classification
Vision System
Lane Detection

Specific Detail Info

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Statement of Responsibility

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