CenterNet Based on Diagonal Half-length and Center Angle Regression for Object Detection

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width. However, because the objects have different sizes, directly regressing their height and width will m...

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Bibliographic Details
Published in:KSII transactions on Internet and information systems 2023-07, Vol.17 (7), p.1841
Main Authors: Xia, Yuantian, Kou, XuPeng, Jia, Weie, Lu, Shuhan, Wang, Longhe, Li, Lin
Format: Article
Language:English
Subjects:
Algorithms
Methods
Object recognition (Computers)
Pattern recognition
Prediction theory
Online Access:Get full text
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Summary:CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width. However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object's width and height, thereby reducing the stability of the model and the consistency of prediction accuracy. For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components. First, encode the object's width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline. Secondly, the predicted diagonal half-length and center angle are decoded into two length components. Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates. Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively. When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets. Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy. Keywords: Object detection, CenterNet, Prediction stability, Accuracy consistency, Convergence speed
ISSN:1976-7277
1976-7277
DOI:10.3837/tiis.2023.07.006