Siemens Aktiengesellschaft (Munich, DE)

Method and system to detect visual anomalies in test images is to generate of multiple iterations that are scaled. An inversion of classifier networks is used to create a current attention map in the event that the forward pass of the test images of the classifier detects an image that is first-class. A portion or all of the attention regions of the current attention map may be extracted and then resized as an image sub-image. Each iteration that is scaled is used to identify some or all of the present attention maps. This can be done with the assumption that the map in use differs significantly from the previous attention map. A number of attention maps are used to visual locate a class on the test picture.

Visual recognition systems may apply machine learning based methods, such as a convolutional neural network, which can involve training the system to recognize features or objects of interest in an image according to learned classifications.Classifications may include tangible properties, such as identifying presence of a particular animate or inanimate thing. The system may be taught to identify objects in pictures.

Visual localization can be combined in conjunction with machine learning to identify anomalies in images or find out if objects are abnormal that might be causing them. Such applications are important to ensure the safety of employees andquality control within the production process. An inspection of abnormalities by employees is an effective method to detect anomalies. It can be accomplished either physically or through watching images taken from camera feeds.

Current methods of machine learning visual localization are based on practical limitations including requiring intensive manual pixel-wise or bounding boxes labeling of images that are training. The labeling process could include creating bounding boxes around the peculiar objects within an image. This is tedious and is not scalable.

Aspects according to embodiments of the present disclosure include a system for visual localization in a test image, which includes at least one storage device storing computer-executable instructions and at least one processor configured toaccess the at least one storage device and execute the instructions to generate, in plurality of scaled iterations, attention maps for a test image using a trained classifier network, wherein the classifier network is weakly supervised using image-levelclassifications. On a condition that forward-passing of the test image through the classifier network detects a particular class, a current map is generated by an inversion of the classifier network. The instructions that are executed may also extract, ineach scaled iteration, one or more regions of the current attention map that have attention values higher than a threshold. It can resize any of the one or more extracted regions as sub-images, in which the size of the sub-image is an incremental increase in size than the extracted region. Every subsequent attention map of respective scaled iterations may be constructed using an inversion of the classifier network for each sub-image on the condition that a forward pass of the sub-image through the classifier network detects thefirst class. Each scaled iteration could necessitate the removal from one or more regions of the current attention map if the current map is substantially different from the previous map. The instructions that are executed may also reveal a visual localization of the initial class of the test image based on one or more of the following attention maps.

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