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An image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depending on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depending on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) this fthis learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depending on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) fthis this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depending on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depending depend on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depend on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible.impossible. (I guess they do, if you are scanning them).

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depend on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible. (I guess they do, if you are scanning them).

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can will be able to detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.

An example image would help to give a more definitive answer, but yes, you can probably automate this process using OpenCV, but you do require some knowledge about it, and there is definitely a learning curve. However, if you use OpenCV for Python (instead of C++) this learning curve is a lot softer, and you'll be able test some algorithms really quickly. It will also depend on how used are you to programming languages in general.

About the problem itself, there are some factors that influence the possibility of automation or not:

• Are the images from PCB's taken in a standard way? Meaning, are all the images taken from the same angle and distance from the PCB's? Or is this random? If the images have a lot of variability in between each other, then the automation process for this can be harder, or even impossible. (I guess they do, if you are scanning them).

• Are the lighting and other environmental conditions in which the images are taken stable? Or they can vary? If they are stable, then your job is easier.

• Do the resistor have some visual property that differentiate them from the rest of the components? If they do, then, in theory, there is some algorithm that can detect them apart from the other components.

There are some computer-vision applications more suited for this kind of industrial problems, and built to be used by non-specialists. I can recomend you to check out Sherlock Vision, its a software built to work on industrial quality inspection problems, it may be quicker to implement a solution.