Advancing emergency vehicle systems with deep learning: A comprehensive review of computer vision techniques

• Deep Learning Transforms Emergency Systems: This review explores how deep learning-based computer vision revolutionizes emergency vehicle detection, enabling real-time tracking, route optimization, and incident analysis in dynamic urban environments. • Cutting-Edge Techniques for Superior Accuracy: Discover advanced methods like CNNs, Siamese Networks, and Attention Mechanisms that outperform traditional approaches, achieving 99.8% accuracy in emergency vehicle classification. • Overcoming Data Scarcity with GANs: Learn how Generative Adversarial Networks (GANs) generate realistic synthetic data, enhancing system robustness in challenging conditions like low light or adverse weather. • Real-World Impact on Urban Safety: See how deep learning reduces response times, prioritizes emergency vehicles in traffic, and improves fleet management, ultimately saving lives in smart cities. • Future-Ready Solutions: Explore emerging trends like multimodal data integration, few-shot learning, and reinforcement learning, paving the way for next-generation emergency response systems. Managing emergency vehicles efficiently is critical in urban areas where traffic jams and unpredictable road conditions can delay response times and put lives at risk. Over the years, machine learning methods like k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), combined with features like HOG and SIFT, paved the way for early image classification and object detection breakthroughs. Tools like Genetic Algorithms (GA) helped refine feature selection, while methods like AdaBoost and Random Forests improved decision-making reliability. The introduction of deep learning has transformed these systems. Convolutional Neural Networks (CNNs) now drive accurate emergency vehicle detection, while Siamese networks support precise identification, such as distinguishing between types of emergency vehicles. Attention mechanisms and Vision Transformers (ViTs) have enhanced the ability to understand context and handle complex scenarios, making them ideal for busy urban environments. Generative Adversarial Networks (GANs) tackle one of the biggest challenges in this field—limited training data—by creating realistic synthetic datasets. This review highlights how these advancements shape emergency response systems, from detecting emergency vehicles in real time to optimizing fleet management. It also explores the challenges of scaling these solutions and achieving faster processing speeds, providing a roadmap for researchers aiming to advance emergency vehicle technologies.