Imaging Assembly for Trailer Door Status Detection
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Solution Overview
Problem
Existing trailer loading systems face delays in accurately determining the status of a trailer door, leading to incorrect load efficiency measurements, especially when trailers are not properly aligned or stopped, causing stacked loads to be misinterpreted as the vehicle trailer door.
Innovation Solution
An imaging assembly comprising a 2D camera and a 3D camera, along with an evaluation module, is used to detect the trailer door status by capturing and processing image data, employing a convolutional neural network to generate alerts and determine if the trailer door is open or closed, ensuring accurate load measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D sensor is used to measure trailer fullness, then load efficiency can be calculated, but detection delay occurs and measurement precision deteriorates when the trailer door status is not accurately determined
Solution Approach 1:
The imaging assembly divides the detection task into multiple components: a 2D camera captures images of the trailer door area, while a 3D sensor measures the fullness of the trailer interior. The evaluation module separately processes door status detection and load fullness measurement, then integrates the results to determine accurate load efficiency metrics.
Solution Approach 2:
The evaluation module acts as an intermediary between the imaging assembly components and the load efficiency calculation system. It receives data from both the 2D camera and 3D sensor, processes and validates the information, and provides accurate door status determination that enables precise load efficiency measurements.
2Ease of operation
If the trailer is not properly aligned or stopped, then operational flexibility is maintained, but measurement precision deteriorates as stacked loads are misinterpreted as the vehicle trailer door
Solution Approach 1:
The system transitions from relying on single-dimensional sensor data to using multi-dimensional information. The 2D camera provides spatial and contextual information about the trailer door area, while the 3D sensor provides depth and volumetric data. This dimensional expansion allows the evaluation module to distinguish between actual door structures and stacked loads even when the trailer is not perfectly aligned.
Solution Approach 2:
The evaluation module changes the parameters used for detection by combining multiple data sources with different characteristics. Instead of using only 3D sensor data that can be ambiguous, the system integrates 2D image data with spatial coordinates, enabling more robust identification of door status under various positioning conditions.
Data Source
AI summary
Disclosed are methods and systems such as an imaging assembly that may include a variety of components, such as, but not limited to, a two-dimensional (2D) camera configured to capture 2D image data, a three-dimensional camera configured to capture three-dimensional (3D) image data, and an evaluation module executing on one or more processors. The 2D camera may be oriented in a direction to capture 2D image data of a first field of view of a container loading area, and the 3D camera may be oriented in a direction to capture 3D image data of a second field of view of the container loading area at least partially overlapping with the first field of view. The evaluation module may be configured to detect a status event in the container loading area based on 2D image data.