Selective Vehicle Payload Imaging for Boulder Detection
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Solution Overview
Problem
Existing methods fail to effectively monitor and analyze the payload in large vehicles to ensure safe and efficient transportation, particularly in mining operations, by detecting boulders and evaluating particle size distribution to prevent equipment damage.
Innovation Solution
An apparatus with a camera and processor system captures and processes images to identify payload regions of interest, using 3D point cloud data, neural networks, and image analysis to detect boulders, foreign objects, and assess payload characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image capture and processing is performed continuously for all vehicles, then payload monitoring coverage is improved, but system resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing images continuously but only processes images that meet specific suitability criteria. Image capture is performed in advance for all vehicles, but detailed processing is selectively applied only when conditions warrant analysis, thus maintaining monitoring coverage while reducing processing time and resource consumption.
2Measurement precision
If detailed payload analysis is performed on all captured images, then measurement precision is improved, but device complexity and processing load increase
Solution Approach 1:
The system applies local quality by performing detailed payload analysis only on specific regions of interest within selected images, rather than processing entire images uniformly. The processor identifies payload regions and applies sophisticated analysis algorithms only to those localized areas, maintaining measurement precision while reducing overall device complexity and processing load.
Solution Approach 2:
The image processing is segmented into multiple stages: initial image capture, suitability criterion evaluation, region of interest identification, and detailed payload analysis. This segmentation allows the system to apply different processing levels to different parts of the data, achieving high measurement precision for critical regions while keeping overall system complexity manageable through hierarchical processing.
3Measurement precision
If 3D point cloud data is generated for all images, then payload detection accuracy is improved, but energy consumption and processing time increase
Solution Approach 1:
The system applies partial action by generating 3D point cloud data only for selected images that meet suitability criteria, rather than for all captured images. This selective approach maintains payload detection accuracy for relevant cases while significantly reducing energy consumption and processing time by avoiding unnecessary 3D reconstruction for images that will not be analyzed.
Data Source
AI summary
An apparatus for analyzing a payload being transported in a load carrying container of a vehicle is disclosed. The apparatus includes a camera disposed to successively capture images of vehicles traversing a field of view of the camera. The apparatus also includes at least one processor in communication with the camera, the at least one processor being operably configured to select at least one image from the successively captured images in response to a likelihood of a vehicle and load carrying container being within the field of view in the at least one image, and image data associated with the least one image meeting a suitability criterion for further processing. The further processing includes causing the at least one processor to process the selected image to identify a payload region of interest within the image and to generate a payload analysis within the identified payload region of interest based the image data associated with the least one image.