How computer vision improves the quality control process

Chris from BoBox

Computer vision in quality control of wood flooring production

Wood-based industry produces billions of products annually.

Computer Vision can be extremely helpful in detecting quality anomalies, as it enables the automation of quality control processes, providing faster, more accurate, and more consistent results compared to manual inspection methods.

This helps ensure that products meet quality standards and reduces the risk of customer complaints or product returns. In addition, computer vision can also be used to monitor production processes and provide real-time feedback to operators, enabling them to quickly address and correct any issues before they result in defective products.

By analysing images of the wood flooring and identifying features that are indicative of quality anomalies, computer vision algorithms can quickly and accurately detect anomalies that might be difficult or time-consuming to identify manually.

Overall, computer vision has the potential to significantly improve the accuracy and efficiency of quality control processes in the wood flooring industry, helping to ensure that high-quality products are produced consistently and reducing the cost and time associated with manual inspection methods.

How to enable CV into quality control?

Computer vision can be used to detect quality anomalies in images. To make this possible, a number of steps need to be taken to get the technology right for our environment and products. The following are some important steps that are extremely important for good implementation of advanced technology.

Prepare the data: Collect a large number of images of wood floors with varying levels of quality, including images that represent both acceptable and unacceptable standards of quality.
Pre-processing: Perform any necessary pre-processing on the images, such as converting them to grayscale or resizing them. This will make it easier to apply computer vision algorithms and reduce the computation time.
Feature extraction: Extract relevant features from the images that can be used to distinguish between acceptable and unacceptable quality. This could involve using techniques such as edge detection, blob detection, or texture analysis to identify anomalies.
Anomaly detection: Use computer vision algorithms to detect anomalies in the images based on the extracted features. This could involve using techniques such as background subtraction, change detection, or object detection.
Classification: Train a machine learning algorithm, such as a support vector machine (SVM) or a convolutional neural network (CNN), on annotated data to classify the anomalies as either acceptable or unacceptable quality.
Validation: Evaluate the performance of the model by applying it to a validation set of images and comparing its predictions to the ground-truth annotations.

The accuracy and effectiveness of the model will depend on the quality and quantity of the annotated data used to train it.

Data is a King!

Data annotation is extremely important in computer vision for quality control.

The accuracy and effectiveness of computer vision models depend heavily on the quality and quantity of annotated data used to train them. Annotation provides the model with labeled information about the objects, features, and attributes present in an image or video, allowing the model to learn how to identify and classify these elements.

In the context of quality control, data annotation can be used to ensure that the images or videos used for training represent real-world scenarios and that the annotated data accurately reflects the features and attributes of interest. Proper data annotation can also help to mitigate issues such as overfitting or bias in the model, as well as improve its performance and accuracy.
In summary, data annotation plays a critical role in the development and success of computer vision models used for quality check by providing labeled information that enables the model to learn and make accurate predictions.

To annotate images for training a wood floor classification model to detect quality exceptions, the following steps should be taken:

Define the annotation task: Clearly define the quality exceptions that the model should be able to detect. This could include things like cracks, knots, or stains in the flooring.
Annotate the images: Annotate each image by creating bounding boxes or masks around the quality exceptions. Assign appropriate labels to each annotation, such as “crack” or “knot.”
Verify the annotations: Verify the annotations to ensure that they are accurate and complete. Consider having multiple annotators review the data to improve the quality of the annotations.
Split the data: Split the annotated data into a training set and a validation set, with a portion of the data reserved for validation. This will be used to evaluate the model’s performance during the training process.
Train the model: Train the computer vision model using the annotated data. This will involve feeding the model the images and their corresponding annotations and adjusting the model’s parameters to minimize the error between the model’s predictions and the ground-truth annotations.

By following these steps, you can create a high-quality annotated dataset that can be used to train a wood floor classification model that accurately detects quality exceptions.

ML models vs OpenCV library?

Machine learning models are not the only choice in many industrial applications we can use OpenCV framework with is is an open-source computer vision library that provides a number of functions for image processing and computer vision tasks, including anomaly detection.

To detect anomalies in wood floor images using OpenCV, we need to pre-process images. This will make it easier to apply computer vision algorithms and reduce the computation time. One of the widely used pipeline in processing data is this:

Take the photo of the component in the real-time from the production. There is needed to have fast & high-res industrial cameras, to have good quality and don’t miss any component in the production process.
First blur the image, e.g. apply a Gaussian blur to the image to reduce noise and make it easier to detect anomalies.
In the next step we should threshold the image, this will separate the anomalies from the rest of the image and make them easier to detect.
Now is the time to detect contours: use OpenCV’s cv2.findContours() function to detect contours in the binary image. The contours will correspond to the anomalies in the image.

Draw contours: Use OpenCV’s cv2.drawContours() function to draw the contours on the original image to visualize the anomalies.
Measure contours: Use OpenCV’s cv2.contourArea() function to measure the area of each contour, which can be used to determine the size of the anomalies.
Classify anomalies: Based on the size and shape of the anomalies, you can use OpenCV’s machine learning algorithms, such as SVM or KNN, to classify the anomalies as either acceptable or unacceptable.

By following these steps, you can use OpenCV’s functions to detect anomalies in wood floor images and classify them based on their size and shape.

Summary

As you can see Computer vision plays a crucial role in quality control by automating the inspection process and providing fast and accurate results, this helps to ensure that products meet quality standards. But to achieve the good results no matter what will be the final technology data annotation is a crucial step in this process.

Annotated data is used to train the machine learning algorithms that are used in the inspection process. The quality of the annotations, including their accuracy and consistency, directly impacts the performance of the algorithms. Therefore, it is essential to invest time and resources into obtaining high-quality annotated data to ensure the success of the computer vision-based quality control system.

Here we come!

Data annotation experts can help to ensure the data is representative of the types of anomalies that need to be detected in the real-world, ensuring that the algorithms are trained on a diverse range of examples. We can also provide quality assurance to ensure that the annotations are accurate and consistent, which is essential for achieving accurate and reliable results from the machine learning algorithms.

In addition, our data annotation experts can help to optimize the data annotation process by selecting appropriate tools and techniques, and by providing training and support to other team members who may be involved in the data annotation process.

Overall, the expertise of our data annotation experts is essential for ensuring the success of computer vision-based quality control systems, as they provide the high-quality annotated data that is necessary for accurate and reliable results!