Semi-supervised instance object detection method based on SVD co-training

WANG Rui; FAN Siyang; XU Jingwen; WEN Zhiqing

doi:10.37188/OPE.20233113.2000

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Semi-supervised instance object detection method based on SVD co-training

Information Sciences | 更新时间：2023-07-17

- Semi-supervised instance object detection method based on SVD co-training
- Optics and Precision Engineering Vol. 31, Issue 13, Pages: 2000-2007(2023)
- 作者机构：
  
  1.北京航空航天大学仪器科学与光电工程学院精密光机电一体化教育部重点实验室，北京 100191
  2.季华实验室智能机器人工程研究中心，广东佛山 528200
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233113.2000
  CLC： TP394.1;TH691.9
- Received：27 July 2022，
  
  Revised：11 September 2022，
  
  Published：10 July 2023
- 稿件说明：
移动端阅览
王睿,樊思杨,许婧文等.采用SVD协同训练的半监督实例级目标检测[J].光学精密工程,2023,31(13):2000-2007.

WANG Rui,FAN Siyang,XU Jingwen,et al.Semi-supervised instance object detection method based on SVD co-training[J].Optics and Precision Engineering,2023,31(13):2000-2007.
王睿,樊思杨,许婧文等.采用SVD协同训练的半监督实例级目标检测[J].光学精密工程,2023,31(13):2000-2007. DOI： 10.37188/OPE.20233113.2000.

WANG Rui,FAN Siyang,XU Jingwen,et al.Semi-supervised instance object detection method based on SVD co-training[J].Optics and Precision Engineering,2023,31(13):2000-2007. DOI： 10.37188/OPE.20233113.2000.

摘要

在室内实例物体目标检测中，传统深度学习需要大量人工标注的训练样本进行网络训练，费时费力，为此提出并实现了一种采用奇异值分解（Singular Value Decomposition，SVD）和协同训练的半监督实例级目标检测网络SVD-RCNN。挑选关键样本进行人工标注并预训练SVD-RCNN，以确保其获取更多先验知识，采用基于SVD的收敛-分解-微调策略，在SVD-RCNN中得到两个较强独立性的检测器以满足协同训练的要求，最后提出一种自适应的自标注策略，获得高质量的自标注及检测结果。在多个室内实例数据集上对该方法进行测试，在GMU数据集上只需人工标注199个样本，均值平均精度（mean Average Precision，mAP）达到了79.3%，相较于需标注3 851个样本的全监督Faster RCNN的81.3% mAP仅下降了2%。消融实验及系列实验证明了本文方法的有效性和普适性，本文提出的方法仅需人工标注5%的训练数据，即可达到与全监督学习相当的实例级目标检测精度，有利于智能机器人高效识别不同实例物体的实际应用。

Abstract

Detecting indoor instance objects is useful for various applications. Traditional deep-learning methods require a large number of labeled samples for network training， making them time-consuming and labor-intensive. To address this problem， SVD-RCNN—a semi-supervised instance object detection network based on singular value decomposition （SVD） and co-training—is proposed. First， key samples are selected for manual labeling to pre-train SVD-RCNN， to ensure that it acquires more prior knowledge. Second， a convergence， decomposition， and finetuning strategy based on SVD is used to obtain two detectors with strong independence in SVD-RCNN to satisfy the requirements of co-training. Finally， an adaptive self-labeling strategy is used to obtain high-quality self-labeling and detection results. The method was tested on multiple indoor instance datasets. On the GMU dataset， it achieved a mean average precision of 79.3% with 199 manually labeled samples. This was only 2% lower than that （81.3%） of Faster RCNN with fully supervised learning， which required labeling 3 851 samples. Ablation studies and a series of experiments confirmed the effectiveness and universality of the method. The results indicated that the method only needs to manually label 5% of the training data to achieve instance-level detection accuracy comparable to that of fully supervised learning； thus， it is suitable for applications in which intelligent robots must efficiently identify different instance objects.

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