ROAD IRREGULARITY DETECTION SYSTEM

Abstract

The objective of the proposed project is to research, explore and develop image classification technique known as Convolution Neural Networks to classify and detect road irregularity. This report explores detailed key ingredients used for classification of road irregularity. Several building blocks of this technique; namely convolution, dynamic feature mapping, non-linearity algorithm, back propagation, inter and inter class classification along with several other layers will be researched and discussed. Eventually a system will be developed using python programming language and android development studio to fully utilize this technique. In our proposed project uses the Convolution Neural Network technique to develop a framework. The main merit of using this technique is its ruggedness to shifts and distortion in the image, fewer memory requirements, better training and high accuracy to name few. Different aspects of this architecture are discussed and dynamic adjustment of weights and feature maps are used to attain better accuracy. After several experiments and adjustments, a proper network will be defined. For beginning a network structure consisting of 3 convolution layer and 2 fully connected feed-forward neural network is used along with numerous dynamically adjusting neurons, weights and feature maps. The system initiates with the pre-processing of input video and will end with classified labels of frames in video. A road map for future development and conclusions are also included in the report.