Combining defect neural network with location neural network

Abstract

In an example embodiment, two different neural networks are used in conjunction with each other to identify defects in items appearing in images. More particularly, a defect neural network is trained on images labeled with an indication of a defect type (or no defect) appears in an item in the image (with optionally an identification of the area in which the defect appears). Separately, a location neural network is trained on images that have a specific geometric shape (e.g., point, line, circle) labeled in the image. The labels identify not just the pixels that are part of the shape, but also provide a value that indicates a degree of the identification of the location. Surrounding pixels to the identified shapes are then given values according to a distribution that is based on distance from the shape, with the peak values being those pixels that are exactly on the identified shape.