Rapid feature detection on multi-surface of metal parts based on six-dimensional pose estimation from a single RGB image

Feature detection on multi-surface textureless metal parts is a common and crucial task in smart manufacturing. Traditional visual detection methods typically detect only one surface at a time, leading to inefficiency. In this work, we propose a framework for the rapid detection of multi-surface features on metal parts based on a non-parallel imaging method. The framework is built upon the principles of wireframe modeling and feature modeling, combining a lightweight neural network with a traditional template matching method. First, the line segment profile features of metal parts are extracted based on a lightweight neural network. The part is then represented by the correlation descriptors of the line segments. The initial bitmap of the part is obtained through template matching, followed by obtaining the accurate six-dimensional (6D) bitmap of the metal part through iterative matching of endpoints. Subsequently, utilizing the 6D pose information, the visible surface of the part undergoes transformation through orthographic projection, and the conformity of the transformed surface is assessed. Finally, experimental validation was conducted using a hydraulic valve as the object. The experimental results indicated that our method accurately estimates the bit-pose of textureless images. Moreover, it can concurrently perform visual detection tasks on multiple visible surfaces by employing a straightforward orthographic projection transformation.