Object Detecting Model for Autonomous Car Driving Using YOLOv3

  • B. Premamayudu


Autonomous cars are the current revolution in the vehicle industry.  It helps ease traffic congestion, prevent accidents and lower pollution.  One of the major challenges in autonomous cars is to accurately detect various objects around the self-driving car (vehicle).  Several Convolutional Neural Network (CNN) techniques in deep learning were proposed to detect the real-time object detection.  This paper demonstrates the object detection model for autonomous driving cars to detect and classify various objects around the car using YOLOv3.  YOLOv3 is an object detection model in deep learning.  It provides a higher accuracy in objects detection and classification in Convolutional Neural Network.  This model uses logistic regression instead of softmax function and filtering with a threshold on class scores.  Object detection model is implemented in tensor flow framework and shown the accuracy results of detection model.

How to Cite
B. Premamayudu. (2020). Object Detecting Model for Autonomous Car Driving Using YOLOv3. International Journal of Advanced Science and Technology, 29(3), 10760 - 10765. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/27882