Real-time Casing Collar Recognition with Embedded Neural Networks
Published:Dec 28, 2025 12:19
•1 min read
•ArXiv
Analysis
This paper addresses a practical problem in oil and gas operations by proposing an innovative solution using embedded neural networks. The focus on resource-constrained environments (ARM Cortex-M7 microprocessors) and the demonstration of real-time performance (343.2 μs latency) are significant contributions. The use of lightweight CRNs and the high F1 score (0.972) indicate a successful balance between accuracy and efficiency. The work highlights the potential of AI for autonomous signal processing in challenging industrial settings.
Key Takeaways
- •Proposes a real-time casing collar recognition system using embedded neural networks.
- •Employs lightweight 'Collar Recognition Nets' (CRNs) optimized for resource-constrained environments.
- •Achieves high accuracy (F1 score of 0.972) with low computational complexity (8,208 MACs).
- •Demonstrates real-time performance with an average inference latency of 343.2 μs.
- •Highlights the feasibility of autonomous signal processing in downhole instrumentation.
Reference
“By leveraging temporal and depthwise separable convolutions, our most compact model reduces computational complexity to just 8,208 MACs while maintaining an F1 score of 0.972.”