Description

Vehicle Identification Using Deep learning for ADAS

The project vehicle detection is implemented for ADAS system.We have used RCNN for successful detection of vehicles. Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection.

Road vehicle detection and classification based on Deep Neural Network

 Published in: 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)