Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Abstract

Increasing deployment of optical fiber networks and the need for reliable high bandwidth make the task of inspecting optical fiber connector end faces a crucial process that must not be neglected. Traditional end face inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. More seriously, the inspection results cannot be quantified for subsequent analysis. Aiming at the characteristics of typical defects in the inspection process for optical fiber end faces, we propose a novel method, “difference of min-max ranking filtering” (DO2MR), for detection of region-based defects, e.g., dirt, oil, contamination, pits, and chips, and a special model, a “linear enhancement inspector” (LEI), for the detection of scratches. The DO2MR is a morphology method that intends to determine whether a pixel belongs to a defective region by comparing the difference of gray values of pixels in the neighborhood around the pixel. The LEI is also a morphology method that is designed to search for scratches at different orientations with a special linear detector. These two approaches can be easily integrated into optical inspection equipment for automatic quality verification. As far as we know, this is the first time that complete defect detection methods for optical fiber end faces are available in the literature. Experimental results demonstrate that the proposed DO2MR and LEI models yield good comprehensive performance with high precision and accepted recall rates, and the image-level detection accuracies reach 96.0 and 89.3%, respectively.

An Effective Multi-Scale Feature Network for Detecting Connector Solder Joint Defects

“…Theoretically, the use of image inspection can assist workers in analyzing welded joint defects and simplify the human visual inspection, thus improving the efficiency of detection and the quality of welding joints [4,5]. Traditional image detection algorithms can usually be divided into three parts: pre-processing, feature extraction, and recognition, among which the design of feature extraction and recognition is particularly important, and a large number of algorithms have been proposed by experts in related fields [6][7][8][9][10], collectively known as automatic optical inspection (AOI). AOI is widely used for the detection of solder joint defects on the surface of printed circuit boards (PCBs), in which industrial cameras are used to obtain images and design different algorithms to complete the task of detecting defects in electronic products, leading to a significant improvement in detection.…”