A Technique to Reduce the Processing Time of Defect Detection in Glass Tubes

Abstract

The evolution of the glass production process requires high accuracy in defects detection and faster production lines. Both requirements result in a reduction in the processing time of defect detection in case of real-time inspection. In this paper, we present an algorithm for defect detection in glass tubes that allows such reduction. The main idea is based on the reduce the image areas to investigate by exploiting the features of images. In our experiment, we utilized two algorithms that have been successfully applied in the inspection of pharmaceutical glass tube: Canny algorithm and MAGDDA. The proposed solution, applied on both algorithms, doesn't compromise the quality of detection and allows us to achieve a performance gain of 66% in terms of processing time, and 3 times in term of throughput (frames per second), in comparison with standard implementations. An automatic procedure has been developed to estimate optimal parameters for the algorithm by considering the specific production process.

Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing

“…In computer vision applications, the first suggested step is related with the definition of the region of interest (ROI) in the image, in order to optimize subsequent analysis. In [15] the authors compare the time reduction in the inspection of glass tubes when the defect detection algorithm is applied to both the original image and the extracted ROI, achieving a performance gain of up to 66%. In terms of processing algorithms, the most common techniques consist in detection of blobs and contours, making it possible to differentiate those pixel clusters that present some quantifiable contrast with the background.…”

“…In terms of processing algorithms, the most common techniques consist in detection of blobs and contours, making it possible to differentiate those pixel clusters that present some quantifiable contrast with the background. Some approaches describe Canny [15][16][17][18], black top-hat [19,20], Watershed [21] and Otsu [22] methods, which successfully extract most of the particles present in the sidewalls. Characterization of the resulting blobs can be achieved analyzing the features [17,20,23] of the connected pixels in those regions.…”

A pre‐processing technique to decrease inspection time in glass tube production lines

“…The technique proposed in this paper reduces the size of the ROI image by excluding areas where no defects are present, and this approach differs from all other works in literature. A preliminary version of the idea appears in [47], where the effects of ROI reduction on processing resources are discussed.…”