Industrial Vehicle Monitoring via Image Analysis

The patent replaces physical scale sensors and cargo detection sensors with computer vision technology. Image capture devices (cameras) mounted on the industrial vehicle capture images of the cargo area, and image processing algorithms analyze these images to detect cargo presence and estimate weight. This substitution eliminates the need for complex mechanical sensor installations while achieving comparable or superior measurement precision.

The system creates visual copies (images) of the cargo and cargo area using cameras, then analyzes these copies to determine cargo presence and characteristics. Instead of directly measuring physical properties with sensors, the system captures optical copies and processes them through image analysis algorithms, simplifying the detection mechanism while maintaining measurement accuracy.

The patent implements a multi-functional monitoring system where a single image capture device and processing platform perform multiple functions: detecting cargo presence, estimating cargo weight, tracking operational hours, monitoring driver presence, and analyzing work patterns. This universal approach eliminates the need for separate specialized sensors for each metric, greatly simplifying installation while maintaining complete information tracking.

The system merges multiple monitoring functions into a unified platform. Image data from cameras is processed by a central processing system that simultaneously extracts multiple metrics (cargo detection, weight estimation, operational tracking). This consolidation of functions into a single integrated system reduces the number of separate components needed and simplifies both installation and operation.

The system performs preliminary calibration by capturing images of known reference objects (cargo items with known weights and dimensions) and using these to train the image processing algorithms. This preliminary action establishes accurate measurement baselines and calibration factors that enable precise weight and dimension measurements from subsequent cargo images, ensuring measurement precision matches or exceeds traditional sensor methods.

The system incorporates feedback mechanisms where image processing results are continuously refined based on comparison with known cargo data and operational patterns. The analytics subsystem uses historical data and machine learning to improve measurement accuracy over time, compensating for variations in lighting, camera angles, and cargo positioning. This feedback loop ensures that measurement precision is maintained and continuously improved without increasing device complexity.