Enterprise AI Analysis
A Real-Time Computer Vision System for Liquid Monitoring in Transparent Containers
This research introduces a novel, real-time computer vision system for liquid monitoring in transparent containers. Leveraging fine-tuned YOLOv11 models and color segmentation, the system effectively addresses challenges posed by transparency, optical distortions, and lighting variability. It achieves high accuracy in detecting containers, estimating liquid levels, and identifying color anomalies, significantly outperforming traditional methods while maintaining real-time performance on standard hardware. The open-source release of code, models, and data sets a new benchmark for advanced monitoring in lab, industrial, and educational settings.
Executive Impact: Enhanced Safety & Efficiency
This system delivers immediate benefits for industries requiring precise liquid monitoring. It reduces human error, automates critical processes, and improves safety protocols across diverse environments.
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Object Detection mAP@0.5
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Liquid Level MAE (Lower is Better)
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Real-time Video Performance
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Custom Annotated Dataset Images
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Container & Liquid Detection
The system leverages fine-tuned YOLOv11 models for robust detection of transparent containers and their liquid contents. Significant performance gains were achieved through domain-specific training over base models.
- Tuned YOLOv11-s achieved best precision (0.923), while YOLOv11-m had best recall (0.856) and YOLOv11-n achieved 0.959 mAP@0.5, showing competitive accuracy with a lighter footprint.
- All fine-tuned models significantly outperformed base models (e.g., YOLOv11-n: mAP@0.5 improved from 0.510 to 0.959).
- Class-wise, bounding box detection showed exceptional performance (>95% mAP@0.5 for containers), with liquid detection slightly lower (94.7-95.4%) due to boundary challenges.
- Segmentation (pixel-level) for liquids (82.0-83.6% mAP@0.5:0.95) outperformed bottles (36.3-38.1%) due to complex material boundaries.
Relevance: Critical for initial identification and localization of monitoring targets, forming the foundation for subsequent analysis in safety and quality control.
Accurate Fill-Level Estimation
The system precisely estimates liquid fill levels by analyzing the geometric relationship between detected container and liquid regions, demonstrating superior accuracy compared to traditional computer vision methods.
- YOLOv11-m achieved the lowest Mean Absolute Error (MAE) at 26.57%, outperforming YOLOv11-n (28.86%) and YOLOv11-s (27.14%).
- All YOLOv11 variants significantly surpassed traditional methods like Canny edge detection (MAE 60.68%), HSV Color Segmentation (MAE 48.40%), and Watershed Segmentation (MAE 42.92%).
- The approach is robust against transparency, reflections, and varying lighting, making it suitable for real-time applications.
Relevance: Essential for quality control, inventory management, and safety applications where precise liquid volumes are critical in industrial and laboratory environments.
Color Anomaly Assessment
A color-based anomaly detection framework identifies potential hazards by classifying liquid colors into low, medium, or high-risk categories, offering a first-pass assessment of safety in transparent containers.
- The YOLOv11-m model showed well-balanced performance for low- (F1-score 0.56) and medium-severity (F1-score 0.52) anomalies.
- High-risk instances were not predicted (Precision, Recall, F1-score 0.00) due to dataset imbalance, indicating a need for targeted sampling or synthetic data augmentation.
- The RGB-based pipeline is efficient and suitable for real-time deployment, providing initial color consistency checks without invasive analysis.
Relevance: Crucial for immediate identification of potentially hazardous or off-spec liquids in industrial and laboratory settings to enhance safety and compliance.
Real-Time System Efficiency
The system is optimized for real-time performance on standard consumer hardware, offering a balance between accuracy and computational load, especially efficient for video processing.
- YOLOv11-n achieved the highest frame rate for images (6 FPS) and videos (9.62 FPS), with lowest inference time (252.96 ms for images, <70ms for videos).
- YOLOv11-m provided a balanced trade-off, achieving 8.18 FPS (images) / 9.13 FPS (videos) with inference times of 179.24 ms / 66.00 ms respectively.
- Memory usage was consistent across models (RAM ~850-890 MB), with VRAM increasing with model size (46.45 MB to 125.59 MB).
- Performance is robust under dynamic conditions including movement, filling, and varying viewing angles, ensuring reliable continuous monitoring.
Relevance: Enables deployment in environments demanding minimal latency for continuous monitoring, such as production lines, laboratory automation, and safety systems.
Implementation Process
Our system follows a multi-stage pipeline designed for real-time operation, integrating YOLOv11 detection, object association, and parallel analytical processes for comprehensive liquid monitoring.
Enterprise Process Flow
Input Acquisition & Preprocessing
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YOLOv11 Tracking (Multi-class Detection, Instance Segmentation, Object Tracking)
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Object Classification & Association (Container-Liquid Pairing)
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Parallel Analytical Processes (Fill Level Calculation, Color Analysis & Classification)
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Anomaly Detection & Real-time Visualization (Liquid Assessment, Live Display)
Relevance: This integrated and efficient design ensures robust, real-time performance, making it suitable for dynamic industrial and laboratory environments.
Calculate Your Potential AI ROI
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Annual Cost Savings
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Annual Hours Reclaimed
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Implementation Roadmap
A phased approach to integrating real-time liquid monitoring into your operations, ensuring a smooth transition and optimal performance.
Discovery & Data Preparation (1-2 Weeks)
Detailed analysis of specific container types and liquid properties. Refinement of data collection and custom dataset annotation based on your operational needs.
Model Fine-tuning & Integration (3-4 Weeks)
Adapting and fine-tuning YOLOv11 models with your domain-specific data. Integrating detection, segmentation, and color analysis modules into a unified system.
System Deployment & Calibration (2 Weeks)
Hardware setup, software installation, and initial calibration to account for specific lighting and environmental conditions in your facility.
Validation & Optimization (1-2 Weeks)
Rigorous testing in real-world scenarios, performance validation, and fine-tuning of parameters to ensure accuracy and real-time efficiency.
Continuous Monitoring & Scaling (Ongoing)
Production deployment, ongoing performance monitoring, and iterative improvements to adapt to evolving requirements and scale across your enterprise.
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