# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 6 Conclusions  
This study presents a comprehensive and practical framework for deploying real-time visual defect detection systems on Raspberry Pi 5 using deep learning. By evaluating three popular CNN architectures--ResNet50, MobileNetV3-Large, and EfficientNet-B3--we demonstrate that lightweight models can achieve high accuracy in detecting geometric and structural defects under real manufacturing conditions using low-cost hardware. Among the models tested, MobileNetV3-Large provided the best deployment trade-off, achieving approximately 8 FPS and 96% classification accuracy. EfficientNet-B3 followed closely with 95% accuracy and ~4.7 FPS, making it a viable option where slightly higher interpretability is desired at the expense of processing speed. ResNet50, despite its excellent offline performance, was not suitable for real-time use without further optimization due to its large size, slower inference, and quantization incompatibility. Key findings include the following:  
* MobileNetV3 is best suited for real-time industrial deployment due to its small size, low latency, and excellent accuracy. * EfficientNetB3 offers improved robustness and interpretability at the cost of lower inference speed, ideal for applications with moderate computational headroom. * Deployment feasibility depends not only on model accuracy but also on architectural compatibility with edge hardware and quantization support. * Conservative prediction behavior, which minimizes false negatives, is preferable in safety-critical environments, even if it increases false positives. Real-time inference on Raspberry Pi 5 at ~8 FPS confirms that affordable, portable AI-based inspection systems are viable for Zero Defect Manufacturing (ZDM). These findingslower the entry barrier for automated quality control and provide a pathway for replacing manual inspections with consistent, explainable machine vision solutions. To address the limitations encountered with ResNet50 and improve model efficiency, future work will explore Quantization-Aware Training (QAT), which integrates quantization into the training process and is known to better preserve accuracy at low bit widths. By retraining heavier models (e.g., ResNet50 or EfficientNetB3) with QAT, it may become possible to retain high accuracy in 8-bit form and enable real-time deployment. Additional techniques such as network pruning, weight clustering, and knowledge distillation will also be investigated. Furthermore, improvements in dataset diversity and balance are essential to mitigate class bias and improve generalization. Expanding the dataset to include more varied defect-free and defective samples across lighting and orientation scenarios will reduce the model's over-reliance on specific visual cues. More adaptable architectures such as edge-optimized transformer models or the use of small hardware accelerators (e.g., TPUs) can also enhance inference throughput. In summary, this research confirms the potential of lightweight deep learning models for reliable real-time defect detection on low-power embedded platforms. With targeted optimization, these models can be further refined to meet the demands of high-throughput and interpretable industrial inspection. Despite the encouraging results, a key limitation of the current study lies in the use of a controlled dataset with predefined defect types, which may not fully capture the variability present in actual production environments. As such, the generalizability of the proposed framework to more complex, real-world industrial scenarios remains to be validated. Future work will prioritize the integration of datasets collected from diverse manufacturing lines including automotive and aerospace sectors to evaluate robustness under varying lighting conditions, material textures, and background complexities. This effort will be essential for advancing the system from a prototype to a field-ready solution for defect detection. [PERSON] helped with conceptualization, software, validation, writing--original draft, resources and data curation.