A doubt–confirmation-based visual detection method for foreign object debris aided by assembly models

Abstract

Foreign object debris (FOD) impacts significantly on the quality control during product assembly because it usually causes product failure. The vision-based method as a nondestructive and efficient technology has become an important approach to FOD detection. However, it faces two important challenges: (1) inexhaustible types (almost any object can become FOD) and (2) unpredictable locations (FOD can appear almost anywhere on surface of a product). Therefore, this paper proposes an FOD visual detection method based on doubt–confirmation strategy and aided by assembly models. Firstly, a coarse-to-fine method is designed for feature extraction and registration to align the test image with the reference image. Then, to solve the unpredictable location problem, different types of suspected FOD are extracted from the test image by a combined method of supervision and nonsupervision. Finally, to solve the inexhaustible type problem, an image comparison method based on a Histogram of Line Direction Angle is proposed, and re-recognition rules of suspected FOD established to complete the final discrimination. Experiments are conducted on a product with complex shape, and the results demonstrate the effectiveness and efficiency of our approach.

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

“…There is limited research on this topic, mainly due to the challenges in vision-based locating of foreign object debris in a complex assembled product, and its recognition falls under the category of open-set problems [3]. Nevertheless, this task can be transformed into a joint task of empirical sampling and pattern comparison within this framework.…”

“…If so, further identification of the type of anomaly is also needed. This demand for flexible capture makes many traditional industrial vision solutions [3][4][5] (as shown in Figure 1a-d) no longer applicable, and the demand for intelligent judgement makes some portable solutions (as shown in Figure 1e) only available for auxiliary visualization and active projection [6,7], while the analysis still relies on humans; Figure 1. The challenges that current vision-based inspection will face in future manufacturing models are already emerging in the appearance quality assurance of the complex product assembly in high-end equipment manufacturing industry.…”