Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification

Abstract

Quality assessment is one of the most common processes in the agri-food industry. Typically, this task involves the analysis of multiple views of the fruit. Generally speaking, analyzing these single views is a highly time-consuming operation. Moreover, there is usually significant overlap between consecutive views, so it might be necessary to provide a mechanism to cope with the redundancy and prevent the multiple counting of defect points. This paper presents a method to create surface maps of fruit from collections of views obtained when the piece is rotating. This single image map combines the information contained in the views, thus reducing the number of analysis operations and avoiding possible miscounts in the number of defects. After assigning each piece with a simple geometrical model, 3D rotation between consecutive views is estimated only from the captured images, without any further need for sensors or information about the conveyor. The fact that rotation is estimated directly from the views makes this novel methodology readily usable in high-throughput industrial inspection machines without any special hardware modification. As proof of this technique’s usefulness, an application is shown where maps have been used as input to a CNN to classify oranges into different categories.