## 1 Introduction  
Industries rely heavily on human inspection for product quality assessment, which is time-consuming and costly [1]. For example, a factory operating 24 h a day with three shifts would require at least three people to inspect product quality. Additionally, inspection accuracy can also be affected by new employees lacking experience and fatigue issues. An automatic inspection system [2] can provide a solution by reducing costs and time and providing a direct correlation between operational information and product quality [3]. An automatic inspection system for identifying defects has several benefits, including operating continuously for extended periods, producing consistent results, and functioning in harsh conditions such as high temperatures and dusty environments [4]. The use of AI in the industrial field has increased in recent years due to advancements in AI methods, open-source frameworks, and decreased development costs; this also eliminates the need for physical computer storage by using the cloud [5, 6]. The COVID-19 pandemic has also contributed to the shift towards automation as it forced a reduction in the number of employees in factories [7]. Deep learning is a popular trend in AI for an automatic inspection system in defect inspection [8]. It is considered a valuable aspect of AI due to its superiority over traditional machine learning methods. Defect inspection using deep learning can effectively identify and classify defects in various materials and products. Deep learning models, particularly convolutional neural networks (CNNs) [9; 10], are particularly effective at image classification tasks and can be trained to recognize and classify defects based on visual characteristics [11]. One advantage of using deep learning for defect inspection is that it is more accurate and consistent than traditional methods [12]. However, some challenges and limitations to using deep learning for defect inspection exist. One of the main challenges is obtaining a large and diverse enough dataset of labeled defects for training the model. Additionally, deep learning models can be computationally expensive to train and deploy, particularly for real-time inspection applications, especially in the real-time inspection of cylindrical shell moving objects [13; 14]. One of the main challenges in the defect inspection of moving cylindrical shell objects is that the object is in motion during the inspection process [13]. This can make it difficult to acquire high-quality images or sensor data that can be used for defect detection. Some specific problems include Image blur: When the object is moving, the images captured during the inspection process may be blurred, making it difficult to detect defects accurately [15]. Sensor data variability: The sensor data collected from a moving object may be more variable than from a stationary object, making it more challenging to train a deep learning model for defect detection [16]. Occlusion: Moving parts of the object may occlude defects, making them difficult to detect. Vibration: The object's vibration during the inspection process can make it difficult to acquire high-quality images or sensor data [17]. Alignment: The object may not be aligned with the sensor during an inspection, making it challenging to acquire accurate images or sensor data. Researchers have proposed various methods to overcome these problems, such as using multiple High-Frame Rate Camera Sensors [18] and advanced deep-learning architectures [19]. Fast-speed cameras can be used in the defect inspection of moving cylindrical shell objects by capturing high-speed images of the object as it moves [20]. These images can then be analyzed to detect defects on the object's surface. One way to use fast-speed cameras for defect inspection is to capture images of the object at a high frame rate and then use image processing techniques to extract features from the images. These features can then be used to train a deep learning model for defect detection.