YOLO: A REAL TIME OBJECT DETECTION ALGORITHM

Visual impairments have become one of the most predominant problems for the last few decades. To keep doing their daily tasks, vision-impaired people usually seek help from others. Hence, over thousands of papers have been published on these subjects that propose a variety of computer vision products and services by developing new electronic aids for the blind. This paper describes CPU Based YOLO, a real time object detection model to run on non-GPU computers that may facilitate the users of low configuration computer. There are a lot of well improved algorithms for object detection such as YOLO, Faster R-CNN, Fast R-CNN, R-CNN, Mask R-CNN, R-FCN, SSD, Retina Net etc. YOLO is a Deep Neural Network algorithm for object detection which is most fast and accurate than most other algorithms. YOLO is designed for GPU based computers which should have above 12GB Graphics Card. In our model, we optimize YOLO with OpenCV such a way that real time object detection can be possible on CPU based Computers. Our model detects object from video in 10.12 – 16.29 FPS and with 80-99% confidence on several Non –GPU computers. CPU Based YOLO achieves 31.05% map.