Recognition of Vehicles, Pedestrians and Traffic Signs Using Convolutional Neural Networks

As the present and future of the robotic world and automation, autonomous vehicles and Advanced Driver Assistance Systems (ADAS) that work in conjunction with autonomous vehicles are important technologies that can benefit drivers through current driving environments. Some elementary factors of these autonomous cars are recognizing surroundings, barriers, pedestrians, traffic signs and other vehicles. In this study, as one of the functions of an autonomous car, the operation of peripheral object recognition is carried out through the use of deep learning which has been mentioned with great accuracy and speed these years in the field of solving problems in machine learning. Signs and objects in various environments, different viewing angles and dimensions can be recognized through the video images taken from the vehicle. Application of object recognition is achieved through the use of 517 images of 10 objects consisting of pedestrians, cars, bicycles and 7 traffic signs, and of convolutional neural networks models including SSD Inception V2, Faster R-CNN Inception V2, Faster R-CNN Resnet 50 and Faster R-CNN Resnet 101, which are known as the basis of deep learning. The models previously trained on the COCO data set are retrained and evaluated on the new data set with the transfer learning method. The new data set is formed by part of the image from the GRAZ-01 and GRAZ-02 data sets and part of the image from the mobile phone camera. As a result of performance analyzes, Faster R-CNN Resnet 101 model is found to be successful in object detection on both images and videos with 85.1% accuracy.