# Vision-Based System for Black Rubber Roller Surface Inspection
## 1 Introduction
Figure 1: Typical defects on black rubber rollers: (**a**) flash defects; (**b**) reverse rubber; (**c**) smaller size rubber; (**d**,**e**) damage; (**f**) corrosion; (**g**) lack of grinding; (**h**) porosity; (**i**) no rubber rollers; (**j**) lack of material at the chamfer; and (**k**) scratches. This research paper presents a machine vision system which has been designed to inspect the surface of black rubber rollers. The system is capable of identifying and localizing major categories of faults on the outer parts of black rubber rollers and can determine whether a black rubber roller meets quality standards, based on the areas and locations of the detected defects. To meet industrial requirements, we suggest a specialized lighting system for the inspection and detection of defects. In addition, we introduce two methods for detecting and classifying defects on the surface of black rubber rollers. The system is capable of replacing manual inspection and exhibits improved performance. This entire surface inspection system is valuable for industrial applications in black rubber roller defect detection and quality evaluation. Vision-based systems have gained popularity in inspecting rubber roller surfaces due to their high accuracy and efficiency in defect detection. Numerous approaches have been proposed for detecting and classifying defects on black rubber roller surfaces. [PERSON] et al. [5] focus on defect detection in rubber rings. They have presented an edge detection algorithm based on digital image processing. The algorithm involves preprocessing the target image to remove noise, utilizing Sobel operators to detect edges, and employing mathematical morphological algorithms for image dilation. The results show the algorithm's effectiveness in repairing fissures, enhancing image brightness, and achieving improved accuracy. [PERSON] et al. [6] have described their work on texture analysis for surface defect detection of rubber oil seals. Their approach includes extracting texture features from grayscale co-occurrence matrices with different spatial correlations. Since computing textural features for the entire image has proved ineffective due to local defect concentrations, the image is segmented prior to feature extraction. They also have proposed and implemented a unique preprocessing method. Similarly, [PERSON] et al. [7] have presented a special algorithm for detecting surface defects on rubber hose surfaces using computer vision technology and the HALCON algorithm. Their method includes defect classification to reduce misjudgment and improve the accuracy of the visual system, resulting in accurate defect detection. [PERSON] et al. [8] have developed an automated optical inspection system for silicone rubber gaskets, utilizing traditional rule-based and deep learning detection techniques. The system aims to detect characteristic defects in gaskets created during the manufacturing process, achieving high accuracy using convolutional neural networks (CNNs) and advanced image capturing and generating approaches. [PERSON] et al. [9] have addressed the challenges of identifying surface defects on oil seals using a visual detection method (VDM). They have proposed an approach of segmenting an image into regions, using gray level changes in the radial direction on the outer part of the oil seal. Additionally, they have introduced a circumferential background difference algorithm considering reflection inequality and low contrast, achieving high recall and precision rates in defect detection. [PERSON] et al. [10] have proposed a novel approach that combines the fringe projection technique with traditional visual inspection devices to overcome their limitations. Their method employs deep learning techniques and introduces the Padua Incremental Mask Labeling Method to accelerate the calibration process. They have designed a one-stage architecture deep learning network called YOLO-OurNet specifically for detecting defects on drum-shaped roller surfaces.