DSpace Repository

Vehicle Detection Using Deep Learning Techniques

Show simple item record

dc.contributor.author Islam, Md. Azharul
dc.date.accessioned 2022-09-04T05:13:21Z
dc.date.available 2022-09-04T05:13:21Z
dc.date.issued 2021-06-20
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8560
dc.description.abstract Vehicle detection and classification using deep learning methods has been found out in this paper. In the area of highway management, vehicle detection and classification are becoming more significant currently. Vehicle Detection and Classification based on Multiple Deep Learning Methods has been found in this paper, multiple classes and multiple methods have been used on this topic in very less research paper. In fact, there are different types of vehicles, such as cars, microbuses, jeeps, pickups, buses, trucks, taxis, vans, rickshaws, etc. Multiple vehicles have different shapes and sizes (bounding boxes) so it is very difficult to detect this multiple class, in this paper multiple classes of vehicle have been used. We have used three of the deep learning methods in this paper, method performance, detection ability and object classification has been compared with those methods. The three deep learning methods we have proposed are Mask R-CNN, Faster R-CNN and Yolo V5 method. Here ResNet50 is used as backbone in Faster RCNN method and ResNet101 is used as backbone in Mask R-CNN method, where Mask R-CNN and Faster R-CNN methods are included in CNN family ties. Though the Mask R-CNN is the extension of Faster R-CNN. We evaluate our models' performance through Confusion Matrix. The methods of F1 score, mean average recall and mean average precision have been found out through the Confusion Matrix, the methods have been compared with those values. From that value it is evident that Mask R-CNN gives better performance than other methods. We see from the table (table: 6) that the following values are obtained using Confusion Matrix from Mask R-CNN method F1 score - 87%, mean average recall- 92% and mean average precision - 82%. So The Mask R-CNN's detection score is higher than other models, so the Mask R-CNN's detection ability and classification is better than other models. There will be a lot of cooperation in vehicle detection and prediction for self-driving cars or various robotic cars through this work. en_US
dc.language.iso en_US en_US
dc.publisher ©Daffodil International University en_US
dc.subject Deep learning en_US
dc.subject Machine learning en_US
dc.subject Vehicle detectors en_US
dc.title Vehicle Detection Using Deep Learning Techniques en_US
dc.type Thesis en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


My Account