## 1 Introduction  
Quality control is one of the most important aspects of industrial processes, ensuring that manufactured products will not harm their users. Critical product defects may cause production interruptions, reduce product lifespan, and result in environmental and economic damages. Regular inspections during both the operational and usage phases are necessary to detect defects as early as possible, so appropriate actions can be taken. Liquefied petroleum gas (LPG) is an environmentally friendly source of energy with wide applications in industrial plants and private households [1]. In recent decades, portable LPG cylinders have been a major source of heat energy in developing countries, particularly in rural areas. After use, LPG cylinders are refilled in industrial plants and returned to the market. It is crucial to identify and scrap surface-defective cylinders before refilling, to maintain quality standards, ensure safety, and uphold the processes for LPG cylinders. The decision to scrap the cylinder is based on the types of defects identified according to the TS EN 1439 standard [2]. These defects are the presence of large and multiple dens on the cylinder body, removal of paint and metallised coating from the cylinder body, and deformations such as multiple intersecting d that disrupt the visual integrity of the body and show colour changes in shades of grey. Such deformations, which may endanger the structural integrity of the cylinder, may prevent safe use of the cylinder and may require the tube to be scrapped. Surface defect detection methods can be broadly categorised into traditional and machine learning-based approaches. Traditional defect detection, which existed before artificial intelligence or machine learning techniques, involved a variety of methods such as manual inspection, non-destructive testing (NDT), and image processing. With manual methods, defects on the product surface are usually detected by the eyes and hands of the responsible personnel. The success of detection is a subjective matter that depends entirely on the expertise and attention of the personnel. It is prone to errors caused by human factors such as fatigue and distraction. However, speed is required to keep up with automated systems [3]. NDT methods such as ultrasonic testing, magnetic particle testing, liquid penetrant testing, and radiographic testing are traditional approaches used to detect internal or surface defects without damaging the material. Although effective, these methods can be time-consuming and require skilled personnel, which can limit their scalability and adaptability to automated processes [4]. The detection of surface defects using image processing is a computer-aided method. These techniques include edge detection, histogram analysis, filtering, and morphological operations [5; 6; 7; 8; 9]. While they can show good results when there is variation between defect colour and background (texture differences in the background, noise, and lighting conditions) [10], their performance is based on manually prepared feature selection and a priori assumptions. Manual feature extraction is may not be suitable for all defects, as these can vary in type, shape, size, and morphology [11]. For example, methods such as edge detection, histogram analysis, and texture analysis are often used in manual feature extraction, but they can pose difficulties in detecting complex or seemingly obscure defects and generalising them to various industrial applications. Traditional approaches to machine learning, including Support Vector Machines (SVM) and K-Nearest Neighbours (KNN), are quite sensitive to environmental factors--like illumination, noise level, complex pattern, or surface texture--but generally less stable with regards to handling changes in these conditions. Hence, deep learning methods have been increasingly being applied for defect detection with more robustness [3; 5; 12].