Truck Cargo Image Recognition for Accurate Gate Intake
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Solution Overview
Problem
Existing cargo identification methods in storage facilities are inaccurate and time-consuming, leading to errors in inventory management and inefficient truck routing.
Innovation Solution
A system using a digital camera and machine learning models to automatically identify cargo on trucks, processing images to determine counts and types, and comparing them with expected values to trigger actions like gate opening or generate reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cargo identification methods are used, then hardware complexity is reduced, but identification accuracy and speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical detection system using digital cameras and machine learning algorithms. The system captures images of cargo on trucks and uses trained neural networks to automatically identify cargo types, counts, and locations, eliminating the need for manual counting and inspection while achieving higher accuracy and speed.
Solution Approach 2:
The patent creates digital copies (images) of the physical cargo instead of directly measuring or counting physical items. By capturing visual representations and processing these digital copies through machine learning models, the system achieves non-contact identification that is both accurate and scalable, avoiding the complexity of direct physical measurement systems.
2Measurement precision
If weighing stations or scales are used for cargo identification, then measurement precision improves, but device complexity and installation difficulty increase
Solution Approach 1:
The patent replaces mechanical weighing stations and scales with an optical imaging system. Instead of using complex mechanical measurement devices that require precise calibration and installation, the system uses digital cameras to capture cargo information, which can be installed simply at gate locations without disrupting existing infrastructure.
Solution Approach 2:
The patent makes the camera system multi-functional by using a single digital imaging device to simultaneously determine cargo type, count, and location. This replaces the need for separate specialized equipment like weighing scales, barcodes, or RFID readers, simplifying installation while maintaining measurement precision through integrated optical detection.
3Productivity
If continuous camera operation is used, then cargo identification speed improves, but energy consumption increases
Solution Approach 1:
The patent implements periodic camera operation triggered by vehicle presence detection rather than continuous operation. The camera activates only when a truck approaches the gate, captures images during the passage, and then remains inactive. This periodic operation mode maintains high identification speed for active cargo detection while dramatically reducing energy consumption compared to continuous operation.
Solution Approach 2:
The system uses feedback from vehicle detection sensors to control camera operation. When a vehicle is detected approaching the gate, the camera is activated to capture cargo information. This feedback-based control ensures the camera operates only when needed, optimizing the balance between identification speed and energy consumption by avoiding unnecessary continuous operation.
Data Source
AI summary
Systems and methods for identifying cargo on a truck include capturing a digital image of cargo on a truck using a digital camera and determining a count of items in the cargo by digitally processing the image of the cargo using a machine learning model trained to identify the items in the image. A unique identifier can be identified in the image corresponding to a type of the items in the cargo, and the type of the items in the cargo can be determined based on the unique identifier. The count of items and the type of items is compared to an expected count and an expected type.