Multitype Label Noise Modeling and Uncertainty-Weighted Label Correction for Concealed Object Detection

Abstract

Recently, plenty of millimeter-wave image concealed object detection models have achieved superior performance on benchmark datasets. The success of these models heavily depends on a large amount of fully annotated data. However, noisy-label data often exist in a large-scale dataset, which deteriorate the performance of models. In this article, we delve into the training of the concealed object detector with label noise, where we recognize divergent types of noisy annotations. To tackle the multitype label noise problem, we propose a unified framework, including two stages: label noise modeling and correction training. We design the region-level label noise modeling, which analyzes and models label noise via the classification branch loss curves. To correct the labels during training, we design the uncertainty-weighted loss for label correction. The experiments on a millimeter-wave security image dataset demonstrate the effectiveness of our proposed training framework, which can alleviate the negative influence of noisy annotations on network training. By introducing our framework, the object detection network can achieve a prominent performance improvement on mean average precision (0.176 on average) compared to the one trained with noisy labels.