Towards Multi-class Detection: A Self-Learning Approach to Reduce Inter-Class Noise from Training Dataset

摘要

This paper proposes a novel self-learning framework, which converts a noisy, pre-labeled multi-class object datasetinto a purified multi-class object dataset with object bounding-box annotations, by iteratively removing noise samplesfrom the low-quality dataset, which may contain a high level of inter-class noise samples. The framework iterativelypurifies the noisy training datasets for each class and updates the classification model for multiple classes. The procedurestarts with a generic single-class object model which changes to a multi-class model in an iterative procedure of whichthe F-1 score is evaluated to reach a sufficiently high score. The proposed framework is based on learning the usedmodels with CNNs. As a result, we obtain a purified multi-class dataset and as a spin-off, the updated multi-class objectmodel. The proposed framework is evaluated on maritime surveillance, where vessels need to be classified into eightdifferent types. The experimental results on the evaluation dataset show that the proposed framework improves the F-1score approximately by 5% and 25% at the end of the third iteration, while the initial training datasets contain 40% and60% inter-class noise samples (erroneously classified labels of vessels and without annotations), respectively.Additionally, the recall rate increases nearly by 38% (for the more challenging 60% inter-class noise case), while themean Average Precision (mAP) rate remains stable.