Deep Learning-Driven Pattern Recognition for Real-Time Traffic Incident Detection in Complex Urban Environments

Ensuring traffic safety and efficient management in densely populated urban environments is increasingly critical as global automobile usage surges. Addressing these challenges requires innovative solutions that leverage advanced mathematical modeling and deep learning techniques. Using an improved version of the Fully Convolutional One-Stage Object Detection (FCOS) neural network optimized with Rep-VGG as its backbone, this study introduces aAnew Intelligent Transportation System (ITS) Afor real-time traffic incident detection. By conducting extensive experiments on the Highway Incidents Detection Dataset (HWID12), the proposed system demonstrates a detection accuracy of 96.91%. AThe integration of advanced deep learning and mathematical modeling techniquesAnot only enhances detection accuracy but also improves computational efficiency, making the system suitable for real-time applications in complex urban traffic networks.