Camera Safety System Distinguishing Welding Sparks
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Solution Overview
Problem
Existing camera-based safety systems for protecting automated machines often result in unnecessary shutdowns due to false detection of small parts or welding sparks, leading to reduced machine availability and production losses, especially in high-speed industrial environments like automotive manufacturing.
Innovation Solution
A device and method using a camera system to classify detected foreign objects by analyzing their characteristic features, such as spatial gray value profiles and movement patterns, to distinguish between safety-critical objects and welding sparks, thereby filtering out welding sparks and preventing unnecessary shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based safety systems detect all foreign objects in the protection area, then safety is improved, but false detection of welding sparks causes unnecessary shutdowns and reduces machine availability
Solution Approach 1:
The system changes the parameters used for object detection from simple presence detection to multi-parameter analysis including spatial gray value gradients, area size, and temporal persistence. By analyzing the gradient of gray values in the spatial domain, the system can distinguish welding sparks (which have characteristic gradient patterns) from actual safety-critical objects, thereby maintaining safety while reducing false shutdowns
Solution Approach 2:
The system applies different evaluation criteria to different characteristics of detected objects. Instead of treating all foreign objects uniformly, it evaluates local properties such as the spatial distribution of gray values, the area occupied by the object, and its persistence over time. This allows the system to selectively respond to objects based on their local characteristics, ignoring welding sparks while responding to genuine hazards
2Reliability
If traditional optoelectronic sensors are used to detect foreign objects, then safety monitoring is achieved, but tiny particles and stray light trigger false shutdown signals
Solution Approach 1:
The system transitions from traditional point-based or line-based optoelectronic sensing to two-dimensional camera-based imaging. This dimensional change allows the system to analyze the spatial distribution, shape, and gray value gradients of detected objects, providing much richer information for distinguishing welding sparks from actual hazards. The camera captures the entire protection area as an image matrix, enabling sophisticated pattern recognition
Solution Approach 2:
The system performs preliminary classification of detected objects using image analysis algorithms before triggering safety shutdowns. By pre-evaluating the spatial gray value gradients, area size, and temporal persistence of detected objects, the system can filter out welding sparks in advance, preventing false shutdowns while maintaining safety monitoring
3Reliability
If multiple scanning cycles are used to suppress transient disturbances, then false detection is reduced, but reaction time increases and safety distances must be larger
Solution Approach 1:
The system changes from temporal filtering (multiple scanning cycles) to spatial filtering (analysis of gray value gradients within single images). By evaluating the spatial distribution of gray values and their gradients across the image matrix, the system can instantly distinguish welding sparks from hazards without requiring multiple time cycles, thus maintaining fast reaction times while suppressing false detections
Data Source
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AI summary
The invention relates to a device for securing a monitoring region (14), in which a machine (16) that operates in an automated manner is arranged. The device (10) has a camera system (12) for monitoring the monitoring region (14), a configuration unit (34) for defining at least one protection region (36) within the monitoring region (14), and an evaluating unit (28) for triggering a safety-relevant function. The camera system (12) provides camera images of the protection region (36), and the evaluating unit (28) evaluates whether a foreign object (53) is present in the protection region (36) or is entering the protection region. The evaluating unit (28) is also designed to classify a foreign object (53) present in the protection region (36) or entering the protection region by analyzing the camera images, in order to determine, on the basis of one or more features characteristic of welding sparks (52), whether the foreign object (53) is a welding spark (52), wherein the evaluating unit (28) is designed to trigger the safety-relevant function if the foreign object (53) was not recognized as a welding spark (52).