Contour Detection Project Application

Project Background

An equipment manufacturing company in the metalworking industry needs to conduct contour detection on its products to filter out defective items.

Project Challenges

The client previously relied on manual visual inspection for contour detection. This method was not only inefficient but also prone to human errors such as missed or incorrect detections. They needed an automated detection system to improve accuracy and efficiency.

Solution

Overall Objective

Add a camera and select appropriate hardware to enable automated contour detection for the customer.

Customer Requirements

1. Field of View (FOV): 50mm x 38mm
   • The FOV should be larger than the product to ensure complete coverage.
2. Working Distance: 95mm
   • The production environment required a working distance of 95mm ± 5mm.
3. Complete Hardware Solution
   • Provide a comprehensive hardware solution that integrates with the software to capture and analyze images.
   • Ensure clear, high-contrast images that emphasize product contours for accurate detection.

Configuration Parameters

1. Project Configuration Table
   • This table outlines the required hardware components, including cameras, lenses, and light sources, based on the required field of view and working distance.
2. Customer-Provided Samples
   • Analyze the samples to determine appropriate hardware selection, focusing on achieving clear and high-contrast images to ensure accurate contour detection.

Solution Overview

Considering the customer requirements, the proposed solution included:

• Working Distance and FOV: Achieved a field of view of 50mm x 38mm, ensuring sufficient coverage for contour detection.
• Hardware Setup: Designed to provide clear and high-contrast images to facilitate software-based contour detection.

Results

• Improved Accuracy: The new solution significantly improved contour detection accuracy while reducing human errors.
• High-Contrast Images: The lighting setup provided clear product contours, facilitating accurate software analysis.
• Reduced Human Error: Automation reduced the risk of missed or incorrect detections, leading to more reliable results.

Effectiveness

Based on the proposed lighting setup and hardware configuration, the system effectively detects and analyzes product contours. The solution meets the customer's requirements for automated contour detection, leading to improved product quality and production efficiency.

Next Steps

• Implement the designed solution in the production environment.
• Test and calibrate the system to ensure reliable performance.
• Monitor and adjust as needed to maintain consistent inspection results.

Additional Considerations

• Software Integration: Ensure the hardware solution integrates smoothly with the existing software to support automated contour detection.
• Calibration and Maintenance: Develop a calibration and maintenance plan to ensure consistent system performance over time.