success story

Computer vision-based edge AI system for remote monitoring

Enabling computer vision-based edge AI solution for real-time equipment monitoring at remote locations. 

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the challenge

Scaling a proof of concept into a production-ready computer vision edge AI solution 

 

The initial proof of concept successfully demonstrated the solution’s feasibility; however, scaling it into a production-ready platform introduced several technical and operational challenges. 

 

Key challenges with the proof of concept included: 

 

  • Hardware-dependent firmware architecture  

  • Inconsistent build and release processes  

  • Limited deployed device visibility  

  • Edge processing and power constraints  

  • No structured AI trade-off evaluation 

These constraints made it difficult to deploy, manage, and continuously evolve the solution across multiple field locations. 
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the initiative

Structured modernization of edge AI platform 

 

To address these challenges, a phased approach was taken to implement edge AI platform to improve scalability and long-term performance. 

 

Key initiatives included: 

 

  • Modular, hardware-agnostic firmware architecture  

  • Unified Windows, NVidia, and x86-64 support  

  • Consistent, reproducible cross-platform builds  

  • Azure IoT-based monitoring and remote management  

  • YOLOv4-based detection benchmarking  

  • Optimized inference through pruning and quantization  

  • Cross-platform AI benchmarking framework  

  • Performance evaluation across OpenCV DNN, ONNX Runtime, and OpenVINO 

 

These enhancements improved deployment stability and system accuracy through automated model retraining. 
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the outcome

Stabilized deployment and improved monitoring capability 

 

The initiative transformed an isolated proof of concept into a managed platform, enabling efficient monitoring, updates, and continuous AI performance improvement. 

 

Key outcomes included: 

 

  • Consistent builds and deployments  

  • Enhanced device visibility  

  • Reliable edge-based video detection 

  • Hardware optimized deployment  

  • Structured AI performance optimization 

With stable architecture and improved efficiency, the company deployed, managed, and scaled edge AI oil spilled monitoring systems in multiple sites.