Abstract: Object detection has been used in many of the field now and it has become the main reason for the development of many applications of the auto driving cars, Statistics and etc. In this paper we will see how the YOLO algorithm works and how it is more efficient than other object detection algorithms using the comparison graphs with the various versions of the YOLO algorithm and other algorithms such as Convolutional Neural Networks, Fast-CNN, etc., In this algorithm, the dataset used for object detection can predefined dataset or dataset manually generated according to the use cases. The experimental data has been taken for the testing of the YOLO algorithm and the dataset is trained and tested with given dataset. Here the image is converted into bounding boxes to which a particular value is given so that it is faster in detecting the images than other object detecting algorithms.
Keywords: Object detection, Fast – Convolution Neural Network, Bounding Boxes, YOLO
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DOI:
10.17148/IJARCCE.2023.12124
