# Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing
## 2 Related Work  
In the sector of glass containers, the physical appearance is the main indicator of the product quality, for both aesthetic and physical reasons. Specifically, in the case of glass bottles, there is plenty of literature, as well as multi-inspection machines, available in the market that aim to detect imperfections in several regions of the geometry to meet the quality standards. The work proposed in this paper is focused on detecting seeds on the walls of the bottles. However, typical defects also include cracks, seams, inclusions or tears in areas like the bottom, mouth and shoulder. Nowadays, machine manufacturers offer different solutions for the inspection of the glass bottles, both in the hot and cold end areas, replacing manual processes which are very time consuming and dependent on subjective perception among the quality experts. Some of these inspection machines are already a standard in the market, as in the case of MCAL4 [7], OMNIVISION 3 [8], FlexInspect [9], EVOLUTION 12 NEO [10] and others such as Imago Omnia [11], FT system [12] and Linatronics AI [13], allowing multiple configurations and high complexity. Even though they are also capable of detecting seeds in the sidewalls, manufacturers don't provide detailed information about either the size of the detected seeds or the accuracy to compare metrics and performance with the method presented in this paper. For instance, some claims regarding the number of bottles processed per hour [8,12] report that over 60,000 bottles/h can be managed by a certain system, without specifying the constrains of that specific scenario (number and size of the defects, number of zones inspected, etc.). In general, these systems can be configurated for a broad range of measurements, including not only defects, but bottle dimensions, deformations, cap presence, liquid fill and others. Meanwhile, the works described in publications reinforces the idea that machine vision is a key technology to tackle the problem of automatizing the inspection task. Ref. [14] lists several use cases where machine vision algorithms are applied to quality control in the glass industry, as well as different techniques widely used to process the images in search of suspicious blobs. 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. 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. Recently, some works applied artificial intelligence [22, 24] for the classification of the blobs, as they provide versatility in the identification of multiple defect classes. To this end, some extracted features from the image were used, which constitute the input of the classifiers. A critical parameter in the quality control of glass bottles is the range of defect sizes that the system can detect. For instance, ref. [25] sets a lower limit of 36 pixels for the blobs in the sidewalls and bottom, although seeds are not included in the study, whereas [16] managed to classify defects as small as 25 pixels, being able to differentiate seeds from other types of inclusions. A better approach is considered to be the specification of the minimum defect size in physical magnitude, as it is closer to the needs of glass manufacturers.