How can you choose the best image processing algorithm for inspection tasks?

Selecting the right image processing algorithm for inspection involves key steps: Define goals, choose techniques (low/high-level), compare algorithm performance, test validity, consider speed, and explore deep learning. Remember, the ideal algorithm suits your unique needs and constraints. Thorough evaluation is crucial.

There are several factors that affect that choice like the desired accuracy, the quality of the image and the inspection type.

To define your method you should understand the nature of the image capture setup. This will provide an intuitive idea about the nature of the image image.As an example, you are asked to detect Vechicle number in a trafic camera which means there is a chance of Blurred images. You can use this image which type of Algorithms you need to use. Understanding the experimental condition is always the best possible way to choose between existing algorithms or to develop a new algorithm.

Establishing a clear understanding of your inspection goals. Defining what needs to be detected, measured. Evaluating performance metrics and accuracy criteria is essential in tailoring the algorithm to meet your specific needs.

I would commence with use cases as this will influence the appropriate image processing technique as well as the tolerance levels for errors. Will the algorithm be used for driverless vehicles, for instance, or to inspect the health of crops. Acceptable risk varies depending on the situation. Establishing baselines for image inspection accuracy and defects is paramount. There should be a consistent process for detecting, measuring and classifying images as well as recording anomalies before application. These steps should help to establish inspection goals.

When I think of image processing techniques, I think of computer vision and the underlying algorithms. I think of object, image and pattern recognition where tasks are automated that would take a human an exorbitant amount of time to probably not complete. Feature extraction and classification techniques are more prevalent in high precision situations such as medical imaging where there is less tolerance for errors and meaningful information from data are of great necessity, e.g., to save lives. Teams should apply image processing techniques that support the goals, including noise ratios, and be careful to balance and not to overly complicate or apply techniques that do not fit the problem to prevent distortions or biased results.

Consider domain expertise: Take into account domain-specific expertise or prior knowledge if available. Certain image processing techniques may be particularly suited to specific domains or industries. Consulting with experts in the field can provide valuable insights into which techniques have proven successful in similar contexts.

As I always say: Less is more most of the time. If we can be reasonably safe to assume a simpler algorithm will perform within our requirements, it's best to go with that. Simpler most of the time means more energy efficient, easier to maintain, faster to develop and more robust. But of course it must be up to the task cut out for it, so first we need to thoroughly assess what is needed to accomplish our goal and make a detailed prioritized list of MEASURABLE requirements our algorithm must fulfill.

Comparing the image processing algorithms involves the following : 1. Theoretical comparison with the help of literature review. or rate each alogorithm based on the literaure review 2. Search for use cases for different situtations. 3. Apply each algorithm in your given situation and rate the performance of each. Consider the following to rate 1. Accuracy 2. Speed 3. Adaptability to different situations.

It's then often advisable to choose at least a "top 3" and test them against each other on a small excerpt of the dataset. This test will not be perfect but can give us an idea how the different algorithms might fare.

I think it’s important to test the image processing algorithms with simulated and real images simply because of the rapid pace of change and unknowns. It is important to include multidisciplinary representation in the testing and validation process including ethics teams. This will influence fine tuning of models through algorithmic refinements. Baselines would have been established but this process may cause revisions. Thorough testing and validation should not be bypassed and should be continuous.

There are so many potential trapdoors here. In most cases, we'd want to test against human level performance, but human labelers are time and cost intensive. Also, humans have their cultural and personal biases they will introduce into the AI, so ideally we'd have a large and diverse group. High accuracy also doesn't necessarily mean we did good. In case of a certain image class being super rare in the real world and our samples, the AI could simply learn to NEVER recognize it and still get a great error score. We need to make sure all classes are accurately represented, both in numbers and variety. At one point we must just trust our instincts and try to think of as many scenarios as possible our AI could fail or step into a trap.

As with all AI Projects: The work is never done "Data Drift" (some parameters or quality of the input data slowly changing on a foundational level) and "Concept Drift" (output of the AI not fitting the real world or our expectations anymore) lurk around every corner. We need to thoroughly understand our domain to be able to anticipate these changes, and also constantly sanity check our actions so we don't overfit towards niche cases. At one point we must also check whether our solution is still applicable or we must start again from scratch.