The client partnered with the team to enhance a CNN-based vehicle detection model, a core component of their smart parking slot monitoring and eco-friendly parking guidance solution. The company provides simple, cost-effective parking guidance systems for shopping centers, strip malls, mega malls, department stores, and other large commercial facilities, helping reduce congestion and environmental impact.
After deployment, real-world conditions caused performance degradation in vehicle presence detection. Through an end-to-end AI deployment and optimization process, including model conversion validation and preprocessing improvements, we restored accurate, production-ready performance across all environments, ensuring reliable AI-powered parking management at scale.
Proposed solutions
Transforming smart parking AI performance
Reducing manual oversight while boosting detection precision
By identifying the root cause of post-export model degradation and optimizing critical components of the inference stack, the team successfully restored—and further enhanced—the system’s real-world accuracy. When evaluated on a targeted dataset of challenging edge cases, where detections had previously failed, the enhanced system achieved a 95% accuracy rate.
To ensure long-term reliability, comprehensive documentation and a fully traceable evaluation framework were delivered. This engagement enabled the client to operate the solution with confidence across diverse environments, significantly reducing the need for manual inspection and strengthening trust in the AI-driven decisions.
As a result, the client unlocked the full potential of their smart parking detection platform, establishing a robust foundation for future scalability and continuous innovation.
Proposed solutions
Transforming smart parking AI performance
Reducing manual oversight while boosting detection precision
By identifying the root cause of post-export model degradation and optimizing critical components of the inference stack, the team successfully restored—and further enhanced—the system’s real-world accuracy. When evaluated on a targeted dataset of challenging edge cases, where detections had previously failed, the enhanced system achieved a 95% accuracy rate.
To ensure long-term reliability, comprehensive documentation and a fully traceable evaluation framework were delivered. This engagement enabled the client to operate the solution with confidence across diverse environments, significantly reducing the need for manual inspection and strengthening trust in the AI-driven decisions.
As a result, the client unlocked the full potential of their smart parking detection platform, establishing a robust foundation for future scalability and continuous innovation.