The detection algorithm module serves as the core engine of the entire intelligent transportation system, built on deep learning and computer vision technologies. This module integrates a variety of specialized algorithm models tailored for rail transit scenarios, capable of accurately identifying personnel behavior, equipment status, and environmental anomalies. The algorithms cover key scenarios such as driver behavior monitoring, track access safety compliance identification, and intelligent diagnosis of pantograph-catenary status, and support real-time early warning of multi-dimensional risks including fatigue, distraction, irregular operations, and equipment failures. All algorithms are trained on massive volumes of on-site railway data, boasting high robustness and scenario adaptability. They can be deployed on on-board edge devices or ground-based servers, delivering precise, reliable, and interpretable intelligent decision support for the safe operation of rail transit systems.