Abstract: Processing image data using deep neural networks is critical to many systems that desire to understand objects and their environment using camera sensors. Image scaling is a fundamental processing task required when managing image data. Although it is possible to scale image data using standard computer or graphics processors it would be highly advantageous in terms of throughput, latency and power consumption to manage image scaling using dedicated neural network hardware. The inventions contained herein provides methods to use existing neural network hardware to preform image scaling functions. Further, the inventions contained herein describe additional circuitry that can be added to neural network hardware to further enhance image scaling capabilities and efficiencies.

Abstract: In an aspect, the present disclosure provides a method of providing a dimensionally aware prediction for an object in an image captured by an image sensor, using a scale selective machine learning system, comprising: obtaining an input comprising image data of an object at an input image scale; generating a plurality of variant images based on re-scaling the input with a plurality of different image scaling factors, each variant image comprising the object at a variant image scale; generating a plurality of scale selective predictions based on the plurality of variant images, and assigning an in-scope response when the variant image comprises the object at an in-scope image scale, and determining a location prediction for the object based on a scale selective prediction comprising the in-scope response.

Abstract: In an aspect, the present disclosure provides a method of generating scale selective training data for use in training a machine learning system to support scale selective image classification tasks, comprising obtaining a plurality of images comprising an object of interest at a plurality of image scales; assigning a desired label to each of the plurality of images based on an image scale of the object of interest in the each image, wherein the desired label comprises an in-scope response when the image scale comprises an in-scope image scale, and generating a set of training data for use in training the machine learning system to predict a scale of the object of interest, the training data comprising the plurality of images and corresponding desired labels.

Abstract: The present disclosures provides systems and methods for generating composite based data for use in machine learning systems, such as for use in training a machine learning system on the composite based data to identify an object of interest. In an aspect, a method of generating composite based data for use in training machine learning systems comprises: receiving a plurality of images, each of the plurality of images having a corresponding label; generating a composite image comprising the plurality of images, each of the plurality of images occupying a region of the composite image; generating a response map for the composite image, the response map having a plurality of response entries, each response entry encoded with a desired label corresponding to a fragment of the composite image, and generating composite data comprising the desired label of a response entry and image data corresponding to the fragment of the composite image.

Abstract: An aggregate-removal method includes placing a metallic article in a tank of fluid, a piece of aggregate being lodged in a feature of the metallic article, and treating the metallic article with sound waves to dislodge the lodged aggregate from the metallic article. An aggregate-identification method includes applying a fluorescent penetrant solution to a metallic article, exposing the metallic article to UV light, and identifying fluorescing aggregate lodged in a feature of the metallic article.

Abstract: A masking tool system includes a first masking body and a second masking body. The first masking body includes a bore passing through a portion of the first masking body and a first sealing element disposed on a first end of the first masking body. The second masking body includes a bore passing through the second masking body and a second sealing element disposed on a first end of the second masking body. The system may also include a rod configured to pass through the bores of the first and second masking bodies and to secure the first and second masking bodies to a metallic article placed therebetween. A diagonal length of the masking tool system induces a wobbling rotation during processing.

Abstract: A masking tool system includes a first masking body and a second masking body. The first masking body includes a bore passing through a portion of the first masking body and a first sealing element disposed on a first end of the first masking body. The second masking body includes a bore passing through the second masking body and a second sealing element disposed on a first end of the second masking body. The system may also include a rod configured to pass through the bores of the first and second masking bodies and to secure the first and second masking bodies to a metallic article placed therebetween. A diagonal length of the masking tool system induces a wobbling rotation during processing.

Abstract: A masking tool system includes a first masking body and a second masking body. The first masking body includes a bore passing through a portion of the first masking body and a first sealing element disposed on a first end of the first masking body. The second masking body includes a bore passing through the second masking body and a second sealing element disposed on a first end of the second masking body. The system may also include a rod configured to pass through the bores of the first and second masking bodies and to secure the first and second masking bodies to a metallic article placed therebetween. A diagonal length of the masking tool system induces a wobbling rotation during processing.