Active Learning for Railway Semantic Segmentation through Ant Colony Optimization

Abstract

In autonomous driving, a tremendous amount of imagery data is collected at all times. Manual annotation of such high-resolution images represents a costly and inefficient process. Active Learning comes to aid annotators in their process to focus on labelling meaningful samples which leads to competitive Machine Learning models, useful for various prediction tasks. In this paper, we introduce a novel Active Learning sampling technique, inspired by the Ant Colony Optimization algorithm, that considers both uncertainty and diversity features. We also introduce two hybrid sampling techniques that use weighted sums. We validate the proposed method on the Semantic Segmentation task, on a popular dataset from the railway domain. We also showcase the effectiveness of Active Learning in the scenario of Rail Semantic Segmentation by using only a quarter of the data to obtain competitive results of up to 78% mean Intersection over Union.