YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications

Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.

I. Introduction

Vehicle object detection fmds many applications in computer vision such as traffic monitoring, traffic density estimation, self-driving cars and many more [1] [2]. Deep convolutional neural networks (DNNs) are used to detect objects in regions. Proposed implementation, YOLOv3 and YOLOv4 algorithm is used to predict class labels and detect objects. Fig. 1.

Methodology of implementation.