This is applied when detection and localisation of instances of visual objects (i.e. defects) of certain classes is required. Image datasets of parts with and without defects are used to train this model.
A class can be created to label each defect/object in an image using a bounding box label to identify and assign it to a target class. The images without defects do not need to be labelled with a class but should be included in the image dataset.
Once trained, the object detection model gives a result of a bounding box around the recognised defect or object in an image. Object detection can be used to produce a test result that will give a count or a pass/fail output.
Manufacturing companies can use object detection to spot defects or missing components in the production line.