To tackle these kinds of challenges, Lilin proposes deploying smart cameras equipped with Aida image recognition technology to achieve efficient and robust track safety monitoring. Lilin's smart cameras integrate AIoT applications and edge computing, enabling real-time video analytics directly at the camera level, eliminating the need for backend servers or AI processing units. This streamlined approach simplifies the monitoring framework and accelerates processing speeds.

Intelligent video analytics and animal intrusion alerts

Powered by deep learning algorithms, the cameras can accurately identify animal types (e.g., cats, dogs, birds) and their behaviours (e.g., lingering or crossing the tracks). Upon detecting an anomaly, the system instantly issues an alert to the control centre, enabling swift action. This proactive alerting mechanism significantly reduces response times, preventing potential train delays or safety incidents. For instance, if a stray dog wanders onto the tracks, the camera immediately triggers an alert, allowing the control center to dispatch personnel promptly to clear the area and maintain smooth operations.

Remote management and unattended operations

Lilin’s solution supports remote management through the Lilinhub App, enabling administrators to monitor the operational status of all cameras in real time and receive push notifications for equipment failures or abnormal events. This allows the control centre to oversee the entire network without deploying large on-site teams, achieving centralised management. Additionally, the cameras feature encrypted cloud communication and automatic updates, ensuring stable performance with minimal on-site maintenance. This unattended operation model reduces operational costs while enhancing management efficiency.

Reduced labour costs

According to the company, compared to traditional monitoring approaches, these smart cameras are able to significantly cut labour requirements. Previously, the light rail relied on scheduled manual inspections or backend staff to review recorded footage. Now, smart cameras handle anomaly detection and alerting at the edge, requiring only a small team at the control centre for follow-up actions. This reduction in labour costs and response times frees up budgets and allows management to re-allocate resources to critical areas such as passenger services or infrastructure upgrades.

Proactive risk prevention

Beyond real-time incident response, Lilin’s intelligent video analytics enable predictive risk management through data analysis. The system logs the frequency and locations of animal intrusions, helping management identify high-risk zones and implement preventive measures, such as installing fences or warning signs. This data-driven approach effectively lowers incident rates, further strengthening the light rail’s safety profile.

Proven success and future prospects

“With smart cameras enabling proactive risk prevention, the frequency of train delays caused by anomalies can be significantly reduced,” said Steve Hu, CIO of Lilin. “The application of smart cameras in rail transport extends beyond safety, offering capabilities like traffic and crowd detection to optimize train scheduling and station management. This not only enhances safety but also boosts operational efficiency.”

Leveraging advanced intelligent video analytics, Lilin has already delivered a highly efficient, secure, and cost-effective monitoring solution for the Kaohsiung Light Rail. Through real-time alerts, remote management, and unattended operations, the system addresses monitoring challenges and mitigates safety risks proactively. This success story underscores the transformative potential of AI in public transportation and provides a replicable blueprint for intelligent management in urban transit systems worldwide.