# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 1 Introduction  
Quality control is an important aspect of the manufacturing sector, which guarantees that products are as stated and fulfill the needs of consumers. The practice entails a combination of processes and methods employed to track, sustain, and enhance product quality along the whole course of manufacturing. The role of quality control cannot be overstated, as it has a direct influence on efficiency in operations, product dependability, and customer satisfaction [1]. Traditional defect detection methods have often depended on manual inspections, which are labor-intensive and prone to human error. In response to these limitations, manufacturing systems are increasingly implementing computerized quality control solutions that utilize artificial intelligence (AI) and machine learning (ML) to improve the accuracy and speed of inspections. Notably, the use of computer vision,especially through deep learning, has become central to automated quality assurance. This transition has significantly enhanced inspection accuracy and efficiency, which are essential for maintaining high manufacturing standards. The integration of these advanced technologies not only streamlines defect detection processes but also reduces operational costs, ultimately leading to improved product quality and customer satisfaction [2]. While surface defect detection has been extensively studied in industrial settings, the automatic identification of geometric anomalies particularly in circular mechanical parts remains a relatively underexplored domain. Existing approaches often rely on perfect circular assumptions or are confined to lens and PCB inspection, without considering manufacturing-induced variations such as roundness deviation, circularity error, diameter deviation, and positional (concentricity) error. Early detection of anomalies, often caused by equipment malfunctions or material defects, is crucial for mitigating financial losses and operational disruptions [3]. Our study addresses this gap by introducing a real-time defect detection framework that captures irregularities and evaluates geometric deviations in circular parts. This system, optimized for lightweight deployment on edge devices, offers a novel and practical solution for defect detection in circular features of manufactured parts by exploring the practical applications of deploying machine learning algorithms on Raspberry Pi 5 for efficient defect detection in various industrial settings. The integration of lightweight models is crucial for effective deployment on resource-constrained devices like the Raspberry Pi 5. Our approach evaluates models like EfficientNetB3, ResNet50, and MobileNetV3 with a focus on establishing a robust baseline for real-time deployment. The objective is to enhance the inline quality control process in manufacturing by predicting future product quality, specifically targeting geometric defects like diameter inconsistency and positional errors. This approach aims to enable early adjustments, minimize scrap production, and reduce downtime in the production system. The goal is to develop a real-time detection system for irregularities in geometric features of metal components using deep learning models to improve production capabilities. The main goals are as follows: (1) develop and evaluate a real-time visual defect detection model can be adapted for industrial applications, with a specific focus on identifying defects in geometrical features such as holes in metal components, (2) improve applicability and scalability of automated quality control in manufacturing environments, (3) address challenges in defect detection, such as dataset imbalance, overfitting, and the ability to generalize to unseen defect types, while ensuring the system is practical for real-time use. Despite the significant progress achieved in visual defect detection using deep learning, most of the existing research focuses predominantly on surface anomalies such as cracks, discolorations, and pits.