Object Detection: Automating Ore Drawing Analysis

Based on Patent Research | CN-110443299-B (2023)

In ore drawing experiments, accurately measuring weight and identifying particles is crucial. Traditional manual methods are often slow and introduce inaccuracies. Object detection, a computer vision task, automates this process by identifying ore particles in images. It determines their color, number, and volume. This technology reduces manual effort, improves data accuracy, and enables better reconstruction of release body shapes. This leads to more efficient and reliable analysis in mining operations.

Manual Drawing Enhanced by AI

For mining and quarrying professionals, object detection offers a powerful solution to challenges in ore dispersion analysis. This technology automates the process of identifying and measuring ore particles in images. The system analyzes visual data, pinpointing individual particles and determining their characteristics. This detailed analysis, including color, number, and volume, generates comprehensive data for mining operations.

This computer vision approach can be integrated into existing imaging workflows, providing automated analysis and reducing manual effort. Consider it like having a geologist manually count rock fragments in a core sample versus a computer instantly identifying and categorizing each mineral grain. Object detection improves data accuracy and optimizes ore processing by enabling better modeling of ore bodies. This technology holds significant potential for optimizing resource extraction and enhancing decision-making in mining operations.

Images to Ore Detection

Capturing Ore Sample Images

Capturing images of ore samples is the first step. High-resolution images or video feeds of ore samples are acquired using specialized cameras or imaging systems, ensuring clear visibility of individual particles. These images serve as the primary input for the AI system.

Enhancing Image Quality

Next, the system enhances the image quality to improve analysis. Image preprocessing techniques, such as noise reduction and contrast adjustment, are applied to the captured images. This ensures the AI model receives clear and well-defined visual data, improving the accuracy of particle detection.

Analyzing Images for Ore Particles

The heart of the process involves analyzing the ore images using a trained AI model. The object detection model, a convolutional neural network, identifies and locates individual ore particles within the images. The model determines key characteristics, such as the color, number, and volume of each particle.

Providing Comprehensive Data Analysis

Finally, the system provides comprehensive data for mining operations. The system aggregates the information about each detected particle, generating data on particle size, shape, color distribution, and volume. This data supports better modeling of ore bodies, optimizing resource extraction and enhancing decision-making in mining operations.

Potential Benefits

Reduced Time and Labor Costs

The AI system automates particle counting and analysis, significantly reducing the time and labor required for manual inspection of ore dispersion.

Improved Accuracy and Consistency

Object detection provides consistent and reliable data, eliminating subjective errors inherent in manual analysis of ore particle characteristics.

Enhanced Data for Decision-Making

By precisely identifying and measuring ore particles, the AI enables more accurate modeling of ore bodies and better resource extraction strategies.

Optimized Ore Processing

Detailed particle analysis, including color and volume, enables optimization of ore processing techniques, leading to higher yields.

Implementation

1 System Setup. Install imaging hardware and software. Calibrate the system for accurate particle capture.
2 Image Acquisition. Establish a protocol for ore sample imaging. Maintain consistent lighting and camera angles.
3 Data Input. Upload sample images to the AI system. Ensure compatibility with the model’s input requirements.
4 Model Configuration. Configure the object detection model. Adjust parameters for specific ore types.
5 Run Analysis. Run the AI analysis on ore images. Review particle data, including color and volume.
6 Data Integration. Integrate data into mining operations. Optimize extraction based on analysis results.

Source: Analysis based on Patent CN-110443299-B "Automatic ore drawing experiment method and system based on image recognition" (Filed: May 2023).

Related Topics

Mining and Quarrying Object Detection
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