Solving the Challenge of Coal-Rock Interface Identification with Image Segmentation

Based on Patent Research | CN-108458989-B (2020)

Accurate identification of coal-rock interfaces is vital for efficient autonomous mining operations. Current approaches often lack the speed and precision required for real-time decision-making, impeding full automation. Image segmentation, a computer vision technique that partitions images into distinct regions (e.g., coal versus rock), offers a robust solution. This method precisely identifies interfaces, enabling rapid decisions for unmanned vehicles. It enhances resource extraction and operational efficiency.

SOLUTION OVERVIEW

Automated Interface Detection: The Manual Alternative

Image Segmentation technology provides a direct solution for the challenges of rapidly and precisely identifying coal-rock interfaces in mining. This computer vision technique begins by taking visual input from sensors positioned at the mine face. It then processes these images, partitioning them into distinct regions. The system automatically classifies these segments as either coal or rock, effectively mapping their boundaries. This process delivers immediate, accurate data, enabling autonomous mining vehicles to make informed decisions without delay.

The practical application of this technology enables continuous, high-speed monitoring of operations, significantly reducing reliance on slower manual checks. Its ability to integrate with existing autonomous systems supports more precise navigation and optimized resource extraction. Consider it similar to how advanced medical imaging automatically highlights tumors in an X-ray; this technology automatically delineates coal from waste rock in a mine face image. Ultimately, this capability promises substantial operational improvements and enhanced decision-making throughout the mining process.

HOW IT WORKS

Finding Coal-Rock Interfaces Through Imagery

Capturing Mine Face Visuals

Sensors strategically positioned at the mine face continuously collect visual input, providing real-time images of the geological environment. This initial step gathers the raw data necessary for the system to begin its analysis of the surrounding terrain.

Segmenting Image Regions

The system processes the captured images using advanced computer vision techniques, partitioning them into distinct, identifiable regions. This process separates the visual information into segments, which prepares the image for detailed analysis.

Identifying Coal-Rock Interfaces

Each segmented region is then automatically classified by the AI system as either coal or waste rock. This crucial step precisely maps the boundaries between different materials, highlighting the coal-rock interfaces.

Enabling Autonomous Decisions

The precise identification data is immediately transmitted to autonomous mining vehicles and control systems. This enables rapid, informed decision-making for navigation, extraction, and optimizing overall operational efficiency.

Potential Benefits

Real-time Precision Mapping

The system accurately identifies coal-rock interfaces instantly, providing continuous, high-speed data for immediate operational adjustments. This precision enables informed decisions for autonomous vehicles.

Boosted Autonomous Operations

By delivering rapid and precise interface data, this technology seamlessly integrates with autonomous mining vehicles. It empowers unmanned systems to navigate and operate more effectively, enhancing overall automation.

Optimized Resource Recovery

Accurate segmentation of coal from waste rock minimizes dilution and maximizes the extraction of valuable resources. This leads to higher yields and more efficient utilization of excavated materials.

Reduced Manual Intervention

Automated coal-rock interface detection significantly decreases the need for slower, manual checks and human oversight. This enhances operational efficiency and safety while lowering labor-related costs.

Implementation

1 Deploy Sensors. Install visual sensors at the mine face. Ensure continuous capture of high-resolution images of the geological environment.

2 Configure Data Stream. Establish data pipelines to transmit sensor visuals to the processing unit for real-time analysis.

3 Integrate AI Model. Embed the image segmentation model. Calibrate it for precise coal-rock interface identification based on mine-specific conditions.

4 Connect Control Systems. Link the AI system's outputs directly to autonomous mining vehicle controls. Enable immediate decision-making and navigation.

5 Validate Performance. Conduct field testing to verify accuracy and speed. Ensure real-time coal-rock interface detection meets operational requirements.

Source: Analysis based on Patent CN-108458989-B "Terahertz multi-parameter spectrum-based coal rock identification method" (Filed: October 2020).