AI-Enhanced Thermal Camera for Transportation Safety and Traffic Management

Abstract: In recent years, the integration of Artificial Intelligence (AI) and Computer Vision (CV) into transportation systems has led to significant advancements, particularly in the domain of safety. AI-driven models have become increasingly adept at image and video recognition, offering the potential to substantially reduce traffic-related accidents and fatalities. Through continuous learning and iterative improvements, these algorithms are able to adapt to evolving traffic conditions, ensuring their long-term effectiveness. The use of AI-powered thermal cameras further enhances the applicability of these technologies for a variety of critical applications. Thermal cameras, when combined with AI and CV technologies, offer notable advantages for video-based safety analytics. They perform exceptionally well in low-light and no-light conditions and are highly robust in harsh environments, such as intense sunlight, glare, or heavy shadows, where traditional cameras often struggle with contrast and clarity. Unlike standard cameras, thermal cameras do not rely on visible light, eliminating the need for continuous artificial lighting. This significantly reduces energy consumption, making them an energy-efficient option for outdoor safety monitoring. Moreover, since thermal cameras capture heat signatures rather than visible images, they help maintain privacy while still providing detailed safety analytics. This makes them particularly valuable in applications where anonymity is a priority. This presentation provides a comprehensive overview of the potential of AI and Computer Vision in enhancing transportation safety through thermal camera technology. It explores the feasibility of implementing this technology, along with its long-term advantages and the potential challenges that may arise.