Vehicle Camera Damage Detection for Warehouse Safety Structures
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Solution Overview
Problem
Current damage detection systems in warehouse environments, such as collision sensors, often generate false alarms and fail to accurately identify damage to safety structures like racking and barriers, posing safety hazards due to their limitations in distinguishing between non-damaging collisions and actual damage.
Innovation Solution
A damage detection system equipped with a camera and controller that processes images to recognize and compare safety structures, providing a damage-status-signal by determining the similarity between acquired images and reference images of undamaged or damaged states, using object recognition and machine learning algorithms, and optionally reading machine-readable codes to identify and assess the condition of safety structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If collision sensors are used to detect damage to safety structures, then damage detection capability is provided, but false alarms increase and measurement precision deteriorates
Solution Approach 1:
The patent replaces mechanical collision sensors with an optical imaging system (camera) combined with image processing and machine learning algorithms. The camera captures images of safety structures, and the controller processes these images to detect damage, substituting the mechanical sensing approach with an optical and computational approach that provides more accurate damage identification and reduces false alarms.
Solution Approach 2:
The system changes the detection parameters by using multiple image processing techniques including object recognition, machine learning classification, and comparison with reference images. Instead of relying on a single sensor threshold, the system analyzes multiple image parameters (visual appearance, structural features, color changes indicating rust) to determine damage status, thereby improving measurement precision.
2Measurement precision
If image processing with machine learning is used to identify damage, then measurement precision improves, but device complexity increases
Solution Approach 1:
The controller is designed to perform multiple functions: capturing images, processing images through various algorithms (object recognition, machine learning classification), comparing with reference images, reading machine-readable codes, and generating damage status signals. By consolidating these diverse functions into a single multi-functional controller, the system achieves high measurement precision while managing device complexity through functional integration.
Solution Approach 2:
The system uses reference images of safety structures in both damaged and undamaged states stored in memory. By comparing captured images against these reference copies, the system can accurately identify damage without requiring complex real-time analysis of all possible damage scenarios, thereby improving measurement precision while keeping the system manageable.
3Measurement precision
If multiple images are combined into 3-dimensional combined-image for comparison, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system combines multiple images of the same safety structure to create a more comprehensive representation (including 3-dimensional combined-images), but only processes images when the vehicle is in proximity to the safety structure. By selectively processing only relevant images rather than continuously analyzing all captured images, the system achieves improved measurement precision through multi-image comparison while minimizing time loss by avoiding unnecessary processing.
Data Source
AI summary
A damage detection system comprising a controller and a camera associated with a vehicle. The camera is configured to acquire images of the vicinity of the vehicle. The controller is configured to process the acquired images in order to: recognise a safety structure in the acquired images; compare the recognised safety structure in the acquired image with an other image of the same, or a corresponding, safety structure; and based on the comparison, provide a damage-status-signal that represents a damage status of the safety structure.