# Vision-Based System for Black Rubber Roller Surface Inspection
## 1 Introduction  
Rubber rollers are vital components of machines that consist of an inner tube or shaft coated with elastomer compounds. They possess numerous desirable properties, including impact strength, shock absorption, compression and deflection, abrasion and chemical resistance, high coefficient of friction, and controllable degree of hardness, making them highly advantageous. They are an ideal choice for handling manufactured goods, causing no harm to the item or the roller itself, unlike metal rollers. In addition, rubber rollers' repair and re-coating are less time-consuming and require less investment than repairing a metal core, thereby extending their service life. They are commonly used in applications that require high surface durability with low to medium hardness, and their proper design and engineering of rubber compounds allow them to withstand mechanical and thermal stresses that may lead to degradation. The primary focus of this research paper is on black rubber rollers, which have extensive use in printers and copiers. These rollers play a vital role in the printing process as they help to transfer ink to paper, and any surface defect can significantly impact the output's quality. As a result, inspection of these rollers is crucial to avoid issues such as heavy downtime, frequent roller replacement, plate wear, and printed material stage. Surface defects on rubber rollers include scarring and damage from mechanical collisions, aging-related corrosion, and lack of material, among others. The rollers' cylindrical surface, chamfers, and end surfaces frequently contain these flaws. The defects are categorized into ten main categories, which include damage, corrosion, material lacking at thechamfer, grind lacking, scratches, porosity, flash defects, reverse rubber, smaller size rubber, and no rubber rollers, as illustrated in Figure 1.  
Figure 1 provides images of the cylindrical surface of a rubber roller, which highlight the importance of detecting surface defects to ensure the roller's performance, stability, productivity, and product quality. Visual inspection is a common method for detecting defects using either the raw human eye or inspection equipment or both these methods. With the development of computers and highly sensitive cameras, visual inspection uses digital images and deep learning to quickly and consistently identify defects in the manufacturing line such as in rubber tire, which is a flexible composite structure composed of rubber and was inspected by [1; 2; 3; 4]. These papers have proposed tire defect classification based on different convolutional networks, which prove the robustness and effectiveness of the improved networks with reduced tire defect detection time. Although visual inspection is a useful tool for detecting defects, it presents several challenges when applied to black rubber rollers. In contrast to various inspection scenarios such as circuit solder inspection, plastic bottle defect detection, and metal product surface defect detection, manual inspection still predominates in the actual production process, leading to low inspection efficiency and accuracy. The conventional inspection methods using light and magnifying glasses and rely on visual analysis, such as edge and line detection, often encounter challenges in accurately identifying and categorizing each type of defect on a rubber roller surface. The reason for this is difficulty in extracting the features of these defects. Typically, defects are treated as arbitrary targets, and in order to highlight them, a difference in reflection of the foreground and the background is needed. However, this method fails to utilize the unique characteristics of each defect, which can lead to the misclassification of certain synthetic marks like oil stains as defects. As a result, this approach can lower accuracy and lead to a low recall rate. Nevertheless, these limitations can be substantially mitigated by utilizing the appropriate lighting system.