# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 2 Related Work  
In manufacturing environments, features can be derived from various sources, including sensor data, images, and historical defect records. However, identifying the most relevant features that contribute to effective classification remains a complex task. The quality of a product may be influenced by different external factors during manufacturing processes, e.g., temperature, humidity, and wear of machines [10]. Understanding how these variables impact defect detection models and incorporating them into the classification process can lead to more stable and trustworthy systems. Real-time visual defect detection has garnered significant attention in recent years, driven by the need for improved quality assurance in Industry 4.0 manufacturing systems. Traditional inspection methods, largely dependent on human labor, are increasingly being replaced by automated systems that leverage computer vision and deep learning to detect defects with high accuracy and consistency. Convolutional Neural Networks (CNNs) have emerged as a cornerstone technology for defect detection in various industrial applications, relying on their capacity to learn intricate features from image data automatically. The effectiveness of CNNs is significantly evidenced in the domain of tire defect detection, where they outperform traditional methods by adapting to the diverse characteristics of images. [PERSON] et al. [11] demonstrated that a CNN could self-learn key features critical for identifying different defect types in tire imagery, achieving notable accuracy through the ensemble of multiple parallel networks. In broader industrial contexts, CNNs have been successfully deployed for detecting metallic surface defects, as illustrated by [PERSON] et al. [12]. Their study highlighted the versatility of CNNs, including adaptations such as multi-scale pyramidal pooling networks, which can handle varying input image sizes, enhancing their utility in real-world applications [12]. Moreover, recent advancements indicate the progress of CNNs in tackling both uniform and non-uniform defects, such as those found in sheet metal stamping. [PERSON] et al. [13] specifically demonstrated the effectiveness of deep learning architectures in detecting split defects, showcasing how tailored CNN approaches can yield superior results over conventional techniques. This adaptability highlights the potential for CNNs to transform defect detection processes across diverse manufacturing environments. ResNet50, a deep residual learning framework, has significantly advanced the field of visual defect detection in manufacturing by enabling the extraction of intricate features from images through its innovative architecture. Introduced by [PERSON] et al. [14], ResNet50 utilizes skip connections or residual connections to facilitate the training of very deep networks, addressing the vanishing gradient problem that often hampers the performance of traditional deep learning models [15]. This architecture has been widely adopted due to its ability to achieve high accuracy with relatively fewer parameters compared to other models, making it suitable for various applications, including industrial defect detection. For instance, in the study by [PERSON] et al. [16], ResNet50 was employed to classify images of steel surfaces with defects, achieving an impressive accuracy of 96.91%. As noted by [PERSON] [15] while ResNet50 can achieve high accuracy, its performance is heavily reliant on the quality and quantity of training data. [PERSON] et al. [17] highlighted the adaptability of ResNet to various datasets, instructing ResNet to generate feature maps and combining them into a multi-level feature fusion network for more accurate defect localization on steel plates. This fusion of features enhances the model's ability to discern distinct defects within complex images, a critical requirement in industrial defect detection. [PERSON] et al.