Recognition of Objects in the Urban Environment using R-CNN and YOLO Deep Learning Algorithms

Over the course of the last decade, the subfield of artificial intelligence, called deep learning, becomes the main technology that provides breakthroughs in the computer vision area. Likewise, deep learning algorithms made a major impact in the automated driving domain. This research aims to apply and evaluate the performance of two pre-trained deep learning algorithms in order to recognize different street objects. Both RCNN, as well as YOLO algorithms, are used to recognize bikes, cars and pedestrians using the public GRAZ-02 dataset composed of 1476 raw images of street objects. Accuracy greater than 90% is achieved in recognizing all considered objects. The fine-tuning and training of both algorithms is established using databases named ImageNet and COCO, and afterwards, trained models are tried on the test data.