Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/95178
Title: Dlafs cascade R-CNN: an object detector based on dynamic label assignment
Authors: Bui, Cao Doanh
Vo, Duy Nguyen
Nguyen, Khang
Keywords: Object detection
Marine vehicle
Cascade R-CNN
Dynamic training
Document detection
Issue Date: 2022
Series/Report no.: Journal of Computer Science and Cybernetics;Vol.38, No.02 .- P.131-146
Abstract: Object detection based on Deep Learning is the revolution of computer science in general and related problems of object detection in particular. In particular, recently, two-stage or multi-stage methods of the R-CNN family have shown outstanding results. These methods have two steps in common: Generating proposal boxes and object classification. In the step of the generating proposal, a Regional Proposal Network (RPN) will be learned to suggest high probability regions in the image, and the part of Label Assignment for RPN is of great interest. If the samples are obtained well, RPN will learn well and help the efficiency of the next stage increase sharply. In this study, we investigate and study to improve the object detection performance when applying Dynamic Label Assignment on the first stage of Cascade R-CNN called DLAFS Cascade R-CNN and perform some experiments to prove the effectiveness. Our DLAFS Cascade R-CNN outperform previous methods on three datasets: SeaShips (+0.2% AP), UIT-DODV (+5.7% AP), MS-COCO (+2.8% AP).
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/95178
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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