Tour buddy (monument detection using yolo)
| dc.contributor.author | Shaikh, Abdus Salam | |
| dc.contributor.author | Khan, Sahijad Ali Sayyed Ali (18DCO06) | |
| dc.contributor.author | Khan, Zaid Mehmood (18DCO07) | |
| dc.contributor.author | Siddiqui, Saif Shamshad (15CO40) | |
| dc.contributor.author | Natiq, Abis Husain Parvez (18DCO12) | |
| dc.date.accessioned | 2021-12-13T05:38:08Z | |
| dc.date.available | 2021-12-13T05:38:08Z | |
| dc.date.issued | 2021-05 | |
| dc.description.abstract | In this paper, we have classified the famous Indian Monuments as India is 7th largest country which has large number of monuments and Historic places where millions of tourists are visits everyday but it is not possible everyone knows everything about those monuments.The paper proposes an approach for classification of various monuments based on the features of the monument images .A well known Convolutional neural network (CNN) Algorithm and YOLO has been adopted to provide on-time and striking accuracies for classifying the Monument images.The Darknet library YOLO has been used for all the training computations as YOLO is a clever convolutional neural network (CNN) for doing object recognition in realtime. The algorithm applies a single neural network to the full image, and then divides the image into multiple regions and predicts bounding boxes and probabilities for each region.After the models gets trained the overall accuracy achieved is 98,Using CNN and YOLO , for a total 20 different monuments that have been considered in the dataset for classifications. Index Terms—YOLO, Convolutional Neural Network ,Deep Learning, Machine Learning , Image recognition . | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3783 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIKTC | en_US |
| dc.subject | Project Report - CO | en_US |
| dc.title | Tour buddy (monument detection using yolo) | en_US |
| dc.type | Project Report | en_US |