Object Recognition System for Autonomous Vehicles Based on PCA and 1D-CNN

Abstract

Object recognition system is an automobile safety system designed for the safety of the autonomous vehicle and other traffic participants and reduces collision risk. Road accidents have long been a significant issue involving loss of life and property. So recent years have seen rapid developments in autonomous and semi-autonomous vehicles. Autonomous vehicles are a comprehensive solution built for safety and comfort on the roads. This solution has many challenges. One of these challenges is to spot and recognize obstacles while navigating. As humans do, the only way to discover and recognize these obstacles is to see them. Therefore, vision systems are an essential part of this type of vehicle. This paper proposed a vision-based system for autonomous vehicles to recognize objects and traffic lights on the road. The proposed system contains three phases: image pre-processing, feature extraction, and classification. In the first phase, some image pre-processing techniques are applied to prepare and improve the input images, consisting of three stages: convert color images to grayscale, histogram equalization, and image resize. In the second phase, extraction of the features from images using Principal Component Analysis (PCA). In the third phase, the extracted features are fed as input to the proposed One-dimensional Convolutional Neural Network (1D-CNN) model for object classification and recognition. The results show that the proposed CNN model achieved a high recognition rate where the classification precision rate reached 100%, and the error rate is 0%. The low number of false alarms and the high precision rate proves that the proposed system performs very well in recognizing the objects.