YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications
Chethan Kumar B., R. Punitha, Mohana
20202020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)101 citationsDOI
Abstract
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.
Topics & Concepts
Computer scienceArtificial intelligenceComputer visionGrayscaleObject detectionImage (mathematics)Object (grammar)TruckRGB color modelArtificial neural networkPattern recognition (psychology)PhysicsThermodynamicsAdvanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques