Active learning-based pedestrian and road sign detection

Fahmida Islam; M. M. Nabi; Mahfuzur Rahman; Iffat Ara Ebu; John E. Ball

doi:10.1117/12.2663694

13 June 2023 Active learning-based pedestrian and road sign detection

Fahmida Islam, M. M. Nabi, Mahfuzur Rahman, Iffat Ara Ebu, John E. Ball

Proceedings Volume 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023; 125400F (2023) https://doi.org/10.1117/12.2663694
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Fahmida Islam, M. M. Nabi, Mahfuzur Rahman, Iffat Ara Ebu, and John E. Ball "Active learning-based pedestrian and road sign detection", Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, 125400F (13 June 2023); https://doi.org/10.1117/12.2663694

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