I utilize a zero shot segmentation method called MobileSAM by good for masking and counting. Use of MobileSAM ensures robust and highly efficient masking, with the use of lower resources compared to its parent SAM architecture. Since it is zero shot, it avoids the time curve and development cycle of creating and labelling datasets for the same. It also provides a option of further enhancement using image prompts.
Non_AI
We utilize a mix of canney edge detection and morphological transforms to enhance the edges in the image. This is followed by a straight forward contour detection to count the objects [in our case, nuts and bolts]. Utilizing this pipeline ensures a much more robust mechanism as each method in the pipeline complement each other and address each of their disadvantages.
Canny Edge Detection provides a edge map but may be noisy or fragmented.
Morphological method clean and enhance the edge map to create more reliable contours.
Contour Detection uses the refined edge map to find the boundaries of objects.
Additional Comments
Loved the assignment as it was very similar to real world scenario problems-something which I have experience dealing with earlier.
Info
Name
KARTHIK K PAI
Python Version
PYTHON 3.12.
Description
AI
I utilize a zero shot segmentation method called MobileSAM by good for masking and counting. Use of MobileSAM ensures robust and highly efficient masking, with the use of lower resources compared to its parent SAM architecture. Since it is zero shot, it avoids the time curve and development cycle of creating and labelling datasets for the same. It also provides a option of further enhancement using image prompts.
Non_AI
We utilize a mix of canney edge detection and morphological transforms to enhance the edges in the image. This is followed by a straight forward contour detection to count the objects [in our case, nuts and bolts]. Utilizing this pipeline ensures a much more robust mechanism as each method in the pipeline complement each other and address each of their disadvantages.
Additional Comments
Loved the assignment as it was very similar to real world scenario problems-something which I have experience dealing with earlier.