# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 2 Related Work
[18] proposed a novel approach for detecting surface defects in hot-rolled steel, using attention mechanisms and dilated convolution to improve precision and generalization. Their two-stage framework, with ResNet50 as the backbone, significantly enhanced feature extraction and detection accuracy. On the NEU-DET dataset, their model achieved a mean Average Precision (mAP) of 81.79%, surpassing several mainstream object detection networks. However, certain defect types remained difficult to detect, raising ongoing concerns in steel manufacturing quality control. EfficientNet, combined with the Lion optimizer, has been effective in the classification of spandex defects with an accuracy of 94.38% and hence has potential for practical applications [19]. The use of EfficientNet in conjunction with novel techniques such as augmentation and transfer learning also shows promise, as authors have observed that these integrations lead to better generalization capabilities in detecting defects on various surfaces [20]. [PERSON] et al. utilized a modified version of EfficientNet as the backbone of their detection model to capture multi-scale features necessary for identifying defects in insulator components [21]. [PERSON] et al. [22] proposed an improved defect detection model, EfficientNet-PSO, for identifying cracks in automobile steel forgings. The model combines EfficientNet with a Feature Pyramid Network (FPN), attention mechanisms, and an enhanced IoU calculation to improve accuracy and convergence. It achieved an mAP of 95.69% and an F1-score of 0.94 with 1.86B FLOPs, outperforming five other deep learning models. A custom image acquisition platform was also developed to validate the model in real-world conditions. [PERSON] et al. [23] proposed a lightweight real-time defect detection network for sanitary ceramics using MobileNetV3 with a multi-layer feature pyramid and a hybrid anchor-free/RPN approach. The model achieved a 22.9% boost in speed, a 35.0% increase in average precision, and reduced memory usage by 8.4%, making it suitable for deployment on low-resource industrial devices. [PERSON] et al. [24] proposed an improved MobileNetV3 with a hybrid attention mechanism for electronic component recognition. It achieved 95.07% accuracy while reducing parameters by 20%, enabling efficient real-time deployment and addressing manual inspection inefficiencies in electronics manufacturing. The lightweight nature of MobileNet enables deployment on platforms with limited processing power, such as Raspberry Pi, without compromising real-time operational capabilities. Moreover, adaptations of MobileNet like the MobileNet-SSD algorithm have shown promise in automotive applications, specifically in detecting paint defects. [PERSON] et al. [25] reported the effectiveness of a lightweight MobileNet-v3 architecture in detecting small target defects within bearing datasets, demonstrating notable improvements in accuracy while reducing the model's computational load. This efficiency is critical for applications in high-speed environments, such as assembly lines, where timely defect detection can significantly reduce waste and enhance quality control [26]. Additionally, [PERSON] [27] showcased a new lightweight model, MobileDDD-Net, which integrates MobileNet for chip defect detection, highlighting MobileNet's adaptability and efficiency in diverse scenarios. Architecture's pruning and quantization techniques further optimize real-time performance, providing a robust solution that remains responsive in demanding industrial settings. Recent advancements in defect recognition have introduced biologically inspired models and adaptive learning frameworks that extend beyond traditional CNN-basedapproaches. For instance, Spiking Neural Networks (SNNs), considered third-generation neural models, offer temporal encoding and sparse event-based processing, making them well-suited for capturing compound mechanical faults. [PERSON] et al.