Machine vision is a critical technology in industrial automation, enabling businesses to analyze images, extract data, and make informed decisions. As the technology evolves, deep learning has emerged as a valuable tool for enhancing Its capabilities. However, machine vision and deep learning systems rely heavily on high-quality images for optimal performance.
Proper lighting is paramount for capturing accurate and repeatable images in machine vision applications. Different applications require specific lighting setups to highlight features of interest and create sufficient contrast. For instance, backlights are ideal for gaging and measurement applications, while line scan lights are essential for web inspection.
Deep learning algorithms excel in optical character recognition, defect detection, and image classification tasks. However, these algorithms require vast amounts of high-quality data to train effectively. Inaccurate or poorly lit images can significantly impact the accuracy and reliability of deep learning models.
Inadequate lighting can lead to “garbage in, garbage out” scenarios in deep learning applications. Poorly lit images can misidentify defects, inaccurate object recognition, and overall system failure. Therefore, selecting the appropriate lighting setup is crucial for the success of any machine vision or deep learning system.
When selecting lighting for a machine vision application, several factors must be considered, including light structure, position, diffusion, and color. The lighting setup should maximize contrast, ensure consistency, and meet the application’s requirements.
Optimal lighting is essential for achieving success with machine vision and deep learning systems. By prioritizing lighting considerations from the outset of the design process, businesses can ensure their industrial automation solutions’ accuracy, reliability, and efficiency.
Related Content: Event-Based Cameras: A New Approach Inspired By Human Vision