Enhancing Urban Safety Through Intelligent Street Lighting: A Review of AI-Driven Detection and Monitoring Approaches

Abstract: With the rapid urbanization and growing demand for public safety, intelligent street lighting systems have become an essential part of smart city infrastructure. In today's street lighting systems, beyond the traditional light provision function, Internet of Things (IoT) and edge computing, artificial intelligence (AI), and computer vision technologies have been integrated to enable real-time monitoring and decision-making. This review paper offers a thorough overview of intelligent street lighting systems and the various detection and monitoring strategies that utilize artificial intelligence (AI) to improve street lighting safety. The study covers the latest developments in IoT-powered sensing, Edge AI, video analytics, deep learning-based object detection, intelligent surveillance, hazard detection, traffic monitoring and emergency response systems. In addition, the review emphasizes that cutting-edge technologies like graph-based networks, federated learning, and explainable AI solutions are crucial for enhancing situational awareness and forecasting urban safety. Important issues such as privacy, cybersecurity, scalability, interoperability, and the limited size of the datasets are examined in detail. The paper also highlights new research opportunities related to lightweight edge intelligence, multimodal sensing, privacy-preserving AI, and integrated smart city platforms. This review, which brings together recent advances from various fields, offers significant insights into the potential of AI-driven intelligent street lighting systems to enhance and transform urban environments into safer, smarter, and more sustainable spaces.

Keywords: Intelligent Street Lighting, Smart Cities, Artificial Intelligence, Internet of Things (IoT), Edge Computing, Computer Vision, Deep Learning,