Water Surface Segmentation with Image Segmentation

Based on Patent Research | CN-117634556-A (2024)

Managing strip mine flood control dams presents ongoing challenges. Inaccurate water surface segmentation can lead to ineffective flood control and environmental risks. Image segmentation, a computer vision task, offers a solution. It uses a semantic segmentation neural network to identify water surfaces in images accurately. This technology enhances flood control, improves resource management, and supports environmental safety. With image segmentation, mining operations can improve the precision of water monitoring.

SOLUTION OVERVIEW

Reimagining Manual Checks with AI Analysis

Image segmentation offers a direct solution for precise water management in the Mining and Quarrying sector. The system begins by gathering visual data from sources like drone imagery or on-site cameras. It then uses a semantic segmentation neural network to analyze these images, accurately delineating water surfaces from other elements, such as soil and vegetation. This detailed segmentation provides a clear, visual representation of water distribution across the mining landscape, enabling proactive flood control.

This technology allows for automated monitoring and integrates easily with existing environmental management systems used in surface mining. Like using a detailed map to navigate a complex mining site, image segmentation provides a precise visual guide for water management. By identifying potential overflow areas early, mining operations can mitigate environmental risks and optimize water resource allocation. This leads to significant operational improvements and better decision-making, paving the way for safer and more sustainable mining practices.

HOW IT WORKS

Processing Dam Images for Segmentation

Capturing Mining Site Imagery

Capturing images of the mining site is the first step. This involves gathering visual data using drones or on-site cameras, providing a comprehensive view of the area. These images serve as the foundation for analyzing water distribution.

Analyzing Images for Water Surfaces

Next, the system analyzes the images using a semantic segmentation neural network. This AI model precisely identifies and delineates water surfaces, distinguishing them from other elements like soil, vegetation, and equipment. The outcome is a segmented image highlighting water areas.

Visualizing Water Distribution

The segmented image provides a clear visual representation of water distribution across the mining landscape. This allows for the identification of potential overflow areas and informs proactive flood control measures. Mining operations can use this information to optimize water resource allocation and mitigate environmental risks.

Potential Benefits

Enhanced Flood Control Management

Enhanced Flood Control Management Accurate water surface identification allows for better prediction and management of potential flooding, protecting mining operations and surrounding environments from damage.

Improved Resource Allocation

Improved Resource Allocation By precisely mapping water distribution, mining operations can optimize water usage, ensuring efficient allocation of this critical resource across different areas.

Reduced Environmental Risks

Reduced Environmental Risks Early detection of potential overflow areas helps mining operations proactively mitigate environmental risks, ensuring compliance with regulations and promoting sustainability.

Streamlined Environmental Monitoring

Streamlined Environmental Monitoring The system automates water monitoring, integrating with existing systems to provide a seamless and efficient solution for environmental management in surface mining operations.

Implementation

1 Image Data Acquisition. Install necessary on-site cameras or configure drone imagery capture for comprehensive site coverage.

2 Data Transfer Setup. Establish a secure data pipeline, transferring images to the processing server or cloud storage.

3 Model Configuration. Configure the semantic segmentation model, specifying parameters relevant to mining environments.

4 Run Segmentation Analysis. Process images to identify and delineate water surfaces, distinguishing them from other materials.

5 System Integration. Integrate segmented water data with existing flood control and environmental management systems.

6 Ongoing Monitoring. Regularly review segmentation results and adjust model parameters to maintain accuracy.

Source: Analysis based on Patent CN-117634556-A "Training method and device for semantic segmentation neural network based on water surface data" (Filed: March 2024).