Spatial information based anomaly detection

A non-transitory computer readable medium for region of interest (ROI) defect detection related to an evaluated manufactured item (MI), wherein the non-transitory computer readable medium stores instructions that cause a processor tor:

• obtain a reference MI image;
• obtain a reference ROI definition;
• obtain the evaluated MI image;
• implement a neural network that is configured to (i) receive the reference MI image and the evaluated MI image to a neural network, and (ii) detect one or more geometrical warping operations that once applied on the reference MI image results in an approximation of the evaluated MI image;
• apply the one or more geometrical warping operations on the reference ROI definition to provide a definition of an evaluated MI image ROI;
• and
• apply an ROI-based defect detection process on the evaluated MI image, based on the evaluated MI image ROI.

A method for region of interest (ROI) defect detection, the method comprising:

• obtaining one or more ROI masks that are related to one or more areas that comprise one or more ROI of an evaluated manufactured item (EMI);
• processing an EMI image by a feature pyramid network (FPN) to provide one or more FPN results;
• wherein the FPN was trained using one or more ROI-masked loss functions;
• and applying the one or more ROI masks on the one or more FPN results to provide one or more ROI-filtered results that emphasize EMI image pixels located within the one or more areas.

A non-transitory computer readable medium for region of interest (ROI) defect detection, wherein the non-transitory computer readable medium stores instructions that cause a processor tor:

• obtain one or more ROI masks that are related to one or more areas that comprise one or more ROI of an evaluated manufactured item (EMI);
• process an EMI image by a feature pyramid network (FPN) to provide one or more FPN results;
• wherein the FPN was trained using one or more ROI-masked loss functions;
• and apply the one or more ROI masks on the one or more FPN results to provide one or more ROI-filtered results that emphasize EMI image pixels located within the one or more areas.

A computerized method for defect detection, the method comprises:

• (a) obtaining an evaluated manufactured item (EMI) image;
• (b) generating multiple probability maps by multiple class subnets,
• different probability maps of the multiple probability maps are associated with different spatial resolutions;
• (c) calculating a merged probability map;
• (d) binarizing the merged probability map to provide a binarized probability map;
• (e) calculating bounding boxes based on the binarized probability map;
• and
• (f) associating defect probabilities with the bounding boxes, wherein the defect probabilities are calculated based on the merged probability map.

A non-transitory computer readable medium for defect detection, wherein the non-transitory computer readable medium stores instructions that cause a processor to:

• (a) obtain an evaluated manufactured item (EMI) image;
• (b) generating multiple probability maps by multiple class subnets, different probability maps of the multiple probability maps are associated with different spatial resolutions;
• (c) calculate a merged probability map;
• (d) binarize the merged probability map to provide a binarized probability map;
• (e) calculate bounding boxes based on the binarized probability map;
• and
• (f) associate defect probabilities with the bounding boxes, wherein the defect probabilities are calculated based on the merged probability map.

A method for anomaly detection, the method comprises:

• obtaining anomaly spatial information regarding anomaly likelihoods at different locations of reference manufactured items;
• obtaining an image of an evaluated manufactured item;
• and
• searching for one or more anomalies in the evaluated manufactured item by applying a location-based anomality detection process that is responsive to the anomaly spatial information.

A non-transitory computer readable medium for animality detection, wherein the non-transitory computer readable medium stores instructions for:

• obtaining anomaly spatial information regarding anomaly likelihoods at different locations of reference manufactured items;
• obtaining an image of an evaluated manufactured item;
• and
• searching for one or more anomalies in the evaluated manufactured item by applying a location-based anomality detection process that is responsive to the anomaly spatial information.

A method for process variation sensitive reference template, the method comprises:

• classifying an evaluated manufactured item, by a classifier, wherein the classifying comprises assigning a score to an image of the evaluated manufactured item;
• wherein the classifying is based on the image of the evaluated manufactured item and on a template image that represents an acceptable manufactured item;
• and
• updating the template image based on an update factor and on the image of the evaluated manufactured item, when the score of the image of the evaluated manufactured item is within a first range.

A non-transitory computer readable medium for process variation sensitive reference template, wherein the non-transitory computer readable medium stores instructions that cause a processor tor:

• classify an evaluated manufactured item, by a classifier, wherein the classifying comprises assigning a score to an image of the evaluated manufactured item;
• wherein the classifying is based on the image of the evaluated manufactured item and on a template image that represents an acceptable manufactured item;
• and
• update the template image based on an update factor and on the image of the evaluated manufactured item, when the score of the image of the evaluated manufactured item is within a first range.

A method for channel reduction of multi-channel descriptors of a layer of a neural network, the method comprises:

• obtaining descriptors that are outputted from the layer of the neural network;
• wherein each descriptor comprises a first number (N1) of descriptor segments from N1 channels;
• calculating, based on the first group of descriptors, a channels cross correlation matrix indicative of correlations between the N1 channels;
• calculating eigenvalues of the channels cross correlation matrix;
• selecting highest value eigenvalues that fulfill a variance condition;
• and
• compressing the descriptors by selecting channels associated with the highest value eigenvalues and removing channels that are not associated with the highest value eigenvalues.

A non-transitory computer readable medium for channel reduction of multi-channel descriptors of a layer of a neural network, wherein the non-transitory computer readable medium stores instructions that cause a processor to:

• obtain descriptors that are outputted from the layer of the neural network;
• wherein each descriptor comprises a first number (N1) of descriptor segments from N1 channels;
• calculate, based on the first group of descriptors, a channels cross correlation matrix indicative of correlations between the N1 channels;
• calculate eigenvalues of the channels cross correlation matrix;
• select highest value eigenvalues that fulfill a variance condition;
• and
• compress the descriptors by selecting channels associated with the highest value eigenvalues and removing channels that are not associated with the highest value eigenvalues.

A computerized system that comprises a processor that is configured to:

• obtain descriptors that are outputted from the layer of the neural network;
• wherein each descriptor comprises a first number (N1) of descriptor segments from N1 channels;
• calculate, based on the first group of descriptors, a channels cross correlation matrix indicative of correlations between the N1 channels;
• calculate eigenvalues of the channels cross correlation matrix;
• select highest value eigenvalues that fulfill a variance condition;
• and
• compress the descriptors by selecting channels associated with the highest value eigenvalues and removing channels that are not associated with the highest value eigenvalues.