Runway incursions pose a serious threat to the safety of aviation and, as such, necessitate proactive and intelligent mitigation measures. Traditional surveillance systems are usually inefficient and far from automated when it comes to real-time risk assessment. Recent developments in AI, IoT, and computer vision have made it possible to create cutting-edge systems for prevention systems. The cloud-enabled monitoring interface uses simple communication with air traffic control for timely action. By utilizing deep learning-based object detection, along with edge computing, the system offers fast and efficient detection of a potential threat. The proposed solution, is designed to work in changing environmental conditions and is highly reliable and scalable. Computer vision enhances situational awareness and reduces human dependency. Tests showthat the system can detect, classify, and sort obstacles with a high level of accuracy. This approach allows for improved operational dependability and will be kept current with contemporary aviation safety regulations. We expect a lot of coverage and accuracy from the detecting algorithms through possible enhancements. The following study emphasizes how AI-based automation can be used to improve airport security protocols.
G et al. (Thu,) studied this question.
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