• HASEEBA YASEEN
• GADIGE RADHA

Object Detection using machine learning has achieved very good performance but there are many problems with images in such as blur or rotating jitter, etc. The aim is to detect objects and real-time objects using You Only Look Once(YOLO) and YOLOv3(YOLO version 3) approach. Object detection in YOLO is done as a regression problem and provide the class probabilities of the detected images. The YOLO methodology has many benefits when compared to other object detection strategies. The biggest advantage of using YOLO is its super speed – it is incredibly fast and can process 45 frames per second. YOLOv3 is a real-time object detection algorithm that identifies specific objects in videos, live feeds or image. We have used this method for detecting various types of objects and created a web application that fetch objects from files and also through web cam to detect using flask framework.

Object Detection, YOLO, YOLOv3, Deep learning, Convolution Neural Network, Bounding boxes.

"YOLO Based Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.e703-e708, May-2022, Available :http://www.jetir.org/papers/JETIR2205585.pdf

"YOLO Based Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppe703-e708, May-2022, Available at : http://www.jetir.org/papers/JETIR2205585.pdf