Vehicle Detection from Acoustic Signals with a Stacked Deep Learning Model

DOI

10.2991/978-94-6239-616-6_77How to use a DOI?

Keywords

Vehicle Detection; Acoustic Signals; Deep Learning; CNN; LSTM; Stacked Models

Abstract

Vehicle detection plays a critical role in traffic monitoring, intelligent transportation systems, smart cities, surveillance, and autonomous vehicles. Traditional approaches, such as camera-based and radar-based systems, often face limitations including poor performance under adverse weather, occlusion, and high deployment costs. Acoustic signal-based vehicle detection has emerged as a complementary solution due to its robustness in low-visibility conditions and cost-effectiveness. Recent advancements in deep learning, particularly stacked models combining Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks, have significantly improved vehicle detection accuracy by effectively capturing spatial and temporal features from acoustic signals. This review analyzes state-of-the-art machine learning and deep learning methods for acoustic-based vehicle detection, emphasizing feature extraction, model architectures, and real-time processing challenges. Key performance evaluation metrics such as accuracy, recall, and F1-score are discussed to highlight the effectiveness of different approaches. Finally, the review identifies open research opportunities, including noise reduction, adaptive learning for diverse traffic conditions, and the development of real-time, scalable, and robust acoustic-based vehicle detection systems for intelligent transportation applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Salika Radha Rukmini
AU  - G. Pratyusha
AU  - Devarasetty Prasad
AU  - R. Raja Ramesh Merugu
AU  - V. Pardhiv Aryan
PY  - 2026
DA  - 2026/03/31
TI  - Vehicle Detection from Acoustic Signals with a Stacked Deep Learning Model
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1057
EP  - 1071
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_77
DO  - 10.2991/978-94-6239-616-6_77
ID  - RadhaRukmini2026
ER  -