Cargo Monitoring Using Multi-View Vision for Pallet Tracking
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Solution Overview
Problem
Existing cargo monitoring systems fail to accurately track and identify cargo during transport, leading to improper loading and delays due to operator-dependent visual identification methods that are inaccurate and time-consuming.
Innovation Solution
A monitoring system using cameras and computing devices to capture and process images of cargo, employing coarse and fine detection techniques, including generalized Hough transforms, to identify cargo features, determine type, and track location within a vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection by operators is used to identify cargo, then the system is simple to operate, but the identification accuracy is low and the process is time-consuming
Solution Approach 1:
The patent replaces the mechanical visual inspection process performed by operators with an automated computer vision system using cameras and image processing algorithms. The system captures images of cargo, performs coarse detection to identify potential cargo regions, then applies fine detection using generalized Hough transforms to accurately identify cargo features and types, eliminating human error and subjectivity in identification.
Solution Approach 2:
The monitoring system performs self-identification of cargo by automatically processing images through detection algorithms. The system independently completes the entire identification workflow from image capture to cargo type determination without requiring operator intervention, making the system self-sufficient in the identification task.
2Productivity
If operators manually input cargo identification into monitoring software, then the system structure is simple, but the loading process becomes slow and delays transport
Solution Approach 1:
The monitoring system continuously captures images of cargo during the loading process and performs real-time processing through coarse and fine detection stages. This continuous automated identification eliminates interruptions and manual input delays, maintaining steady workflow and accelerating the overall loading process compared to intermittent manual inspection.
Solution Approach 2:
The manual input process is replaced with automated image processing that instantly identifies cargo and inputs data into the monitoring software. The system processes images through detection algorithms and automatically generates identification results, eliminating the time-consuming manual data entry step entirely.
3Measurement precision
If existing monitoring systems are used, then the system is simple to implement, but the cargo position tracking is inaccurate and loading may be improper
Solution Approach 1:
The detection process is divided into two sequential stages: coarse detection that identifies potential cargo regions by removing background sections, and fine detection that precisely identifies cargo features using generalized Hough transforms. This segmentation allows the system to achieve high position tracking accuracy by progressively refining detection from rough localization to precise feature identification.
Solution Approach 2:
The system transitions from simple presence detection to multi-dimensional analysis by examining cargo from different views using multiple cameras. The computing device processes images to identify cargo position, orientation, and dimensions, adding spatial dimensions to the tracking accuracy beyond simple binary detection.
Data Source
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AI summary
A monitoring system to monitor cargo on a vehicle. The monitoring system includes cameras configured to capture images of the cargo while on the vehicle with the cameras aligned to capture images of the cargo from different views. A computing device includes processing circuitry configured to process the images received from the cameras. The computing device is configured to: identify a base of the cargo; track a location of the cargo within the vehicle based on a position of the base within the images; determine that the cargo is a pallet; and determine a volume of the pallet.