Systems and methods to optimize performance of a machine vision system

1. A method for optimizing a performance of a machine vision system, the method comprising:

obtaining, by one or more processors, one or more first images of a target object, each of the one or more first images including a pass indication output by a previously executed machine vision job that analyzed each of the one or more first images;

obtaining, by the one or more processors, one or more second images of the target object, each of the one or more second images including a fail indication output by the previously executed machine vision job that analyzed each of the one or more second images;

preparing a new and/or adjusted machine vision job that includes one or more machine vision tools configured to perform one or more machine vision techniques to each of the one or more first images and the one or more second images;

conducting, by a feasibility setup tool executed on the one or more processors, a feasibility setup analysis by (i) performing the one or more machine vision techniques of the new and/or adjusted machine vision job on each of the one or more first images and each of the one or more second images and (ii) generating a respective updated result indication for each of the one or more first images and each of the one or more second images;

comparing, by the feasibility setup tool, the respective updated result indication to (i) the respective pass indication for each of the one or more first images and (ii) the respective fail indication for each of the one or more second images; and

based on the comparing, generating, by the feasibility setup tool, one or more suggestions to optimize the performance of the machine vision system.

2. The method of claim 1 , wherein conducting the feasibility setup analysis further includes generating a respective quality value for each of the one or more first images and each of the one or more second images.

3. The method of claim 2 , wherein the respective quality value may include an indication of one or more of (i) a brightness level, (ii) a contrast level, (iii) a feature edge strength value, or (iv) a focus value.

4. The method of claim 2 , wherein the one or more suggestions include at least one of (i) a suggestion based upon the respective quality value of a respective image from the one or more first images or the one or more second images or (ii) a suggestion based upon an aggregate quality value of a plurality of images from the one or more first images or the one or more second images.

5. The method of claim 1 , further comprising:

displaying, on a graphical user interface (GUI), each of the one or more suggestions for viewing by a user;

obtaining, by the feasibility setup tool, a user input indicating an acceptance of one or more of the one or more suggestions; and

responsive to the user input, automatically adjusting, by the feasibility setup tool, a feature indicated in the one or more of the one or more suggestions.

6. The method of claim 1 , wherein the feasibility setup tool applies a sequence of a set of machine vision tools, each tool including a configuration to perform one or more of the one or more machine vision techniques.

7. The method of claim 6 , wherein the one or more suggestions include at least one of (i) an adjustment to the configuration of a respective machine vision tool, (ii) an adjustment to the sequence of the set of machine vision tools, or (iii) an adjustment to a set of imaging device settings.

8. The method of claim 7 , wherein each of the set of imaging device settings includes one or more of (i) an aperture size, (ii) an exposure length, (iii) an ISO value, (iv) a focus value, (v) a gain value, or (vi) an illumination control.

9. The method of claim 6 , wherein the one or more machine vision tools include at least one of (i) a barcode scanning tool, (ii) a pattern matching tool, (iii) an edge detection tool, (iv) a semantic segmentation tool, (v) an object detection tool, or (vi) an object tracking tool.

10. A computer system for optimizing a performance of a machine vision system, the system comprising:

one or more processors; and

a non-transitory computer-readable memory coupled to the imaging device and the one or more processors, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to:

obtain one or more first images of a target object, each of the one or more first images including a pass indication output by a previously executed machine vision job that analyzed each of the one or more first images,

obtain one or more second images of the target object, each of the one or more second images including a fail indication output by a previously executed machine vision job that analyzed each of the one or more second images,

prepare a new and/or adjusted machine vision job that includes one or more machine vision tools configured to perform one or more machine vision techniques to each of the one or more first images and the one or more second images, and

execute a feasibility setup tool configured to:

conduct a feasibility setup analysis by (i) performing the one or more machine vision techniques of the new and/or adjusted machine vision job on each of the one or more first images and each of the one or more second images and (ii) generating a respective updated result indication for each of the one or more first images and each of the one or more second images,

compare the respective updated result indication to (i) the respective pass indication for each of the one or more first images and (ii) the respective fail indication for each of the one or more second images, and

based on the comparing, generate one or more suggestions to optimize the performance of the machine vision system.

11. The computer system of claim 10 , wherein conducting the feasibility setup analysis further includes generating a respective quality value for each of the one or more first images and each of the one or more second images.

12. The computer system of claim 11 , wherein the respective quality value may include an indication of one or more of (i) a brightness level, (ii) a contrast level, (iii) a feature edge strength value, or (iv) a focus value.

13. The computer system of claim 11 , wherein the one or more suggestions include at least one of (i) a suggestion based upon the respective quality value of a respective image from the one or more first images or the one or more second images or (ii) a suggestion based upon an aggregate quality value of a plurality of images from the one or more first images or the one or more second images.

14. The computer system of claim 10 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

display, on a graphical user interface (GUI), each of the one or more suggestions for viewing by a user;

obtain a user input indicating an acceptance of one or more of the one or more suggestions; and

responsive to the user input, automatically adjust a feature indicated in the one or more of the one or more suggestions.

15. The computer system of claim 10 , wherein the feasibility setup tool applies a sequence of a set of machine vision tools, each tool including a configuration to perform one or more of the one or more machine vision techniques.

16. A tangible machine-readable medium comprising instructions for optimizing a performance of a machine vision system that, when executed, cause a machine to at least:

obtain one or more first images of a target object, each of the one or more first images including a pass indication output by a previously executed machine vision job that analyzed each of the one or more first images;

obtain one or more second images of the target object, each of the one or more second images including a fail indication output by a previously executed machine vision job that analyzed each of the one or more second images;

preparing a new and/or adjusted machine vision job that includes one or more machine vision tools configured to perform one or more machine vision techniques to each of the one or more first images and the one or more second images; and

execute a feasibility setup tool configured to:

conduct a feasibility setup analysis by (i) performing the one or more machine vision techniques of the new and/or adjusted machine vision job on each of the one or more first images and each of the one or more second images and (ii) generating a respective updated result indication for each of the one or more first images and each of the one or more second images,

compare the respective updated result indication to (i) the respective pass indication for each of the one or more first images and (ii) the respective fail indication for each of the one or more second images, and

based on the comparing, generate one or more suggestions to optimize the performance of the machine vision system.

17. The tangible machine-readable medium of claim 16 , wherein conducting the feasibility setup analysis further includes generating a respective quality value for each of the one or more first images and each of the one or more second images.

18. The tangible machine-readable medium of claim 17 , wherein the respective quality value may include an indication of one or more of (i) a brightness level, (ii) a contrast level, (iii) a feature edge strength value, or (iv) a focus value.

19. The tangible machine-readable medium of claim 17 , wherein the one or more suggestions include at least one of (i) a suggestion based upon the respective quality value of a respective image from the one or more first images or the one or more second images or (ii) a suggestion based upon an aggregate quality value of a plurality of images from the one or more first images or the one or more second images.

20. The tangible machine-readable medium of claim 16 , wherein the instructions, when executed, further cause the machine to at least:

display, on a graphical user interface (GUI), each of the one or more suggestions for viewing by a user;

obtain a user input indicating an acceptance of one or more of the one or more suggestions; and

responsive to the user input, automatically adjust a feature indicated in the one or more of the one or more suggestions.