Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
3194-3200

Positive and negative samples allocation algorithm for object detection models incorporating ratio-prior and loss-aware

Zhuang Xujun1
Zuo Huahong2
Han Ping1
1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2. Wuhan Chuyan Information Technology Co. , Ltd. , Wuhan 430030, China
DOI: 10.19734/j.issn.1001-3695.2023.01.0013

Abstract

To address the shortcomings of the object detection model in the training process, such as the allocation of positive and negative samples without considering the aspect ratio of the ground-truth box and the poor adaptability to different distributions of objects, this paper proposed the ratio-prior and loss-aware assignment(RLA) algorithm. RLA didn't change the structure of the original detection model, firstly it selected an equal proportion of the central sampling area based on the aspect ratio of the ground-truth box, then calculated the integrated loss of the anchor points, considered the actual distribution of objects within the ground-truth box, and finally distinguished between positive and negative samples by means of a dynamic loss threshold. The algorithm solved the problems of poor adaptability and difficulty in selecting the best positive samples based on IoU allocation, and the sample allocation for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample allocation algorithms, the algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm respectively when the model structure is the same, demonstrating the effectiveness of the RLA algorithm.

Foundation Support

国家自然科学基金资助项目(51405360)
中央高校基础研究基金资助项目(WUT:2018III069GX)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.01.0013
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Technology of Graphic & Image
Pages: 3194-3200
Serial Number: 1001-3695(2023)10-048-3194-07

Publish History

[2023-03-16] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

庄旭君, 左华红, 韩屏. 融合比例先验和损失感知的目标检测模型的正负样本分配算法 [J]. 计算机应用研究, 2023, 40 (10): 3194-3200. (Zhuang Xujun, Zuo Huahong, Han Ping. Positive and negative samples allocation algorithm for object detection models incorporating ratio-prior and loss-aware [J]. Application Research of Computers, 2023, 40 (10): 3194-3200. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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