Solving the Challenge of Dark Mining Target Detection with Object Detection

Based on Patent Research | CN-117789169-A (2024)

Effective target detection in dark, complex mining environments supports operational safety and efficiency. Current methods often struggle to identify objects accurately in low-light conditions, hindering autonomous system performance. Object detection technology directly addresses this by locating and classifying objects of interest. This approach uses image fusion and depth estimation to provide clear situational data. It improves the safety and efficiency of operations within these challenging settings.

SOLUTION OVERVIEW

Manual Detection Meets AI Detection Technology

For mining and quarrying operations, Object Detection technology directly addresses the challenge of identifying targets in dark, intricate environments. This system gathers visual data, which is then processed using image fusion to combine information from various sensors for enhanced clarity. Simultaneously, depth estimation algorithms analyze spatial relationships. These combined inputs enable the precise location and classification of critical objects, such as equipment, personnel, or geological features, providing clear situational awareness for improved operational safety and efficiency.

This capability supports autonomous systems by providing reliable real-time environmental data, reducing reliance on manual inspections in hazardous zones. The technology can integrate seamlessly with existing mine management platforms, enhancing overall operational control and decision-making. Imagine a sophisticated guidance system for a haul truck, not merely sensing light, but actively recognizing a sudden rockfall or a person in its path, even in the deepest underground tunnels. This advancement significantly elevates operational reliability and worker safety across the entire mining workflow.

HOW IT WORKS

Capturing critical targets from video

Capturing Environmental Data

The system collects raw visual inputs from multiple sensors, including visible light and infrared cameras. This diverse data acquisition is crucial for perceiving objects accurately across varied lighting conditions typical of mining environments.

Enhancing Scene Clarity

Acquired images undergo sophisticated image fusion, combining visible and infrared data to overcome low-light challenges and improve overall visibility. Simultaneously, depth estimation algorithms process these fused images to calculate the precise distance and spatial relationships of objects within the scene.

Identifying Critical Objects

Using advanced deep learning models, the system analyzes the enhanced and depth-informed visual data. It then precisely locates and classifies targets such as operational equipment, personnel, or potential hazards like rockfalls, even in complex, cluttered backgrounds.

Delivering Operational Insights

The identified objects and their spatial data are presented as real-time situational awareness, directly informing autonomous systems and human operators. This actionable intelligence improves decision-making, significantly enhancing safety and efficiency across mining operations.

Potential Benefits

Enhanced Safety for Personnel

The system accurately identifies personnel and equipment in low-light, complex mining environments. This reduces risks by providing real-time awareness, preventing accidents in hazardous zones.

Improved Operational Efficiency

By providing precise object detection and situational awareness, autonomous systems can operate more reliably. This leads to smoother workflows and reduced downtime in mining and quarrying tasks.

Reliable Data for Autonomy

Image fusion and depth estimation deliver consistent, high-fidelity environmental data. This critical input empowers autonomous vehicles and machinery to navigate and perform tasks safely.

Better Decision-Making Capabilities

Integrating this technology with mine management platforms offers comprehensive situational data. Managers gain insights to optimize resource allocation and respond proactively to changing conditions.

Implementation

1 Deploy Sensor Hardware. Install visible and infrared cameras in strategic mining locations. Ensure secure, robust mounting and power connectivity.

2 Configure Processing Engine. Set up the software for image fusion, depth estimation, and deep learning models. Calibrate initial system parameters.

3 Integrate Data Streams. Connect the processed object detection outputs to existing mine management and autonomous control systems.

4 Validate System Performance. Conduct rigorous testing and validation in actual mining environments. Fine-tune for accuracy and reliability.

5 Operational Monitoring. Utilize real-time insights to enhance safety, guide autonomous systems, and improve overall operational decision-making.

Source: Analysis based on Patent CN-117789169-A "Image fusion-based target detection method in mining area scene" (Filed: March 2024).