Abstract
Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).
Highlights
Introduction and MotivationThe data imbalance commonly encountered in deep network training can have a profound effect on the training process and detection capability of the network [1]
The indices of objects are duplicated according to a sampling ratio input by an expert in the field, where the sampling ratio is determined by the degree of data imbalance of datasets; to be less dependent on the ratio, we propose the balanced focus loss function in the subsection
The green curve obtained using the balanced focus loss function reached 96.35% at epoch 62, i.e., 5% higher than the baseline method with less fluctuation
Summary
Introduction and MotivationThe data imbalance commonly encountered in deep network training can have a profound effect on the training process and detection capability of the network [1]. The number of data points pertaining to the objects differs significantly from the number of data points pertaining to the background. Under these conditions, the network often learns to detect only the background and large surface objects; i.e., it tends to skip smaller objects. Growing interest in deep learning has brought the problem of data imbalance to the foreground, in the field of data mining [3], medical diagnosis [4], the detection of fraudulent calls [3], risk management [5,6,7], text classification [8], fault diagnosis [9,10], anomaly detection [11,12], and face recognition [13]. For the imbalanced data problem, there were three main methods: data-based, algorithm-based, and ensemble methods
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