The Boeing Company

Richard A Effler

Alexander Stephen Burch

A computing device is configured to detect inconsistencies on a vehicle that are typically small and difficult to detect using conventional inspection techniques. Particularly, a spotlight illuminates a target area on the vehicle. The color of the spotlight is the same color as the ambient light illuminating the vehicle but is a higher intensity. Additionally, the color of the spotlight and the ambient light is selected based on the type of inconsistencies expected to be detected. Images of the target area are then captured, digitally processed, and stored in memory, such as a database, for example. Based on the processing of these images, a weighting coefficient is computed and used to generate an enhanced contrast feature map. Inconsistencies are then detected based on the enhanced contrast feature map.

Method for large area inspection

Software-based solutions may mitigate physical damage to multi-layer networks, such as neural networks having shortcut (residual) connections. An example includes: providing a multi-layer network comprising a plurality of nodes; for each of a plurality of training cases: determining a set of dropout nodes, based at least on a damage model having a probability of a node being selected for dropout that is based at least on a target operating environment of the multi-layer network, wherein the probability of a node being selected is spatially correlated; and training the multi-layer network with the determined set of dropout nodes disabled (with a different set of dropout nodes for different training cases). In some examples the damage model involves expected physical radiation damage to a computing device hosting the multi-layer network, such as on board an aircraft or an earth-orbiting satellite. Thus, multiple degrees of expected damage may be addressed.

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O'Fallon, MO, United States of America

Richard A Effler