Active learning-based pedestrian and road sign detection

Abstract

For autonomous driving, pedestrian and road signs detection are key elements. There is much existing literature available addressing this issue successfully. However, the autonomous system requires a large and diverse set of training samples and labeling in real-world environments. Manual annotation of these samples is somewhat challenging and time-consuming. In this paper, our goal is to get better detection accuracy with minimal training data. For this, we have employed the active learning algorithm. Active learning is a useful method that selects only the effective portion of the dataset for training and reduces annotation costs. Though it uses only a small amount of the training data, it provides a high detection accuracy. In this work, we have chosen the deep active learning model for object detection via the probabilistic model of Choi et al. and modified the depth scale of different layers in the backbone. As real-world data may contain noise, motion, or other disruptions, we modified the original model to obtain improved detection results. In this experiment, we create a customized dataset that contains pedestrians, road signs, traffic lights, and zebra (or pedestrian) crossings to deploy the active learning algorithm. The experimental results show that the active learning model can produce good detection outcomes by accurately detecting and classifying pedestrians, road signs, traffic light, and zebra (or pedestrian) crossings.

Inconsistency-based active learning with adaptive pseudo-labeling for fish species identification

“…Several strategies have been proposed to tackle this challenge and minimize annotation costs, including semi-supervised learning, 18 weakly-supervised learning, 19 zero-shot learning 20,21 and active learning. 22,23 These methods offer a crucial benefit in their ability to reduce data labeling expenses by leveraging the model's capacity to generate pseudo-labels through learning from labeled datasets.…”