Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

Algorithm 1

Algorithm for Hyb-SMPC

	Input: D: Labeled dataset D = {(), (), (), …, ()} with p images
	B = {, , , …, } with q classes
	M = {, , …, with j models
	IM = input images
	Output: Trained models
	= labels of Classes for the SKUs included in input images
	Start:
	= Split (D, p ∗ 80)
	= Split (D, (p-(p ∗ 80)))
	//Step 1—Training of models with labeled data
	for n = 1 to j:
	for every epoch:
	for every () in:
	= Train ()
	end
	end
	end for
	//Step 2—Testing models
	for k = 1 to j:
	for every () in :
	Prediction =  ()
	end
	end for
	//Step 3—Detecting SKUs in input imagebˆ = TM ()
	Output: Processed images (PI)
	//Step 4—Sorting SKUs and Racks
	PPI = Sorting (PI)
	//Step 5—Generating Planogram from JSON object and comparing post processed image with Planogram layout
	JO = contour (Pg)
	foreach in DCompare (, JO)
	End Algorithm