# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 6 Conclusions and Future Work  
In this research, an exploration of few-shot object detection methods based on fine-tuning was conducted, addressing the challenge of detecting defects on the surface of steel. Through a series of enhanced experiments, the precision of the model was significantly improved to meet industrial production standards. The proposed methodology demonstrated  
Figure 8: Visualization of results: Green for pits, yellow for cracks, purple for scratches, and red for iron oxide scales. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **FPS (Frames per Second)** & **7-Shot mAP** \\ \hline Faster R-CNN (VGG16) & 46.9 & 55.72\% \\ Faster R-CNN (ResNet50) & 69.7 & 66.92\% \\ Faster R-CNN (ResNet101) & 67.1 & 72.28\% \\ YOLOv4 & 95.4 & 54.82\% \\ Proposed & 63.2 & 76.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the models. Figure 7: Comparison of few-shot object detection using different models. a notable increase in the detection accuracy for small-target defects within the dataset, compared with the typical fine-tuning based few-shot target detection framework. Additionally, the method showed improvements in detecting medium and large targets. The co-tributions of this study are concentrated in the following areas:  
1. An in-depth analysis and visualization of the dataset were executed, encompassing the quantification of diverse defect categories with respect to their count and pixel scale. In compliance with the criteria set for few-shot datasets, a tailored dataset for few-shot defect detection was established. A comparative evaluation of various backbone networks within the paradigm of fine-tuning few-shot target detection methods was undertaken. The selection of ResNet101 within the Faster R-CNN framework emerged as the most suited approach for the backbone network in subsequent experiments. 2. This study improved the SSL algorithm's workflow that was based on pseudo-labeling. The incorporation of the locally linear embedding (LLE) algorithm for data cleansing was proven effective in enhancing the model's detection accuracy. 3. Refinement of few-shot defect detection algorithms: Given the extensive pixel scale diversity in the dataset, feature pyramids and attention mechanisms were introduced. These modules underwent the relevant refinement, resulting in a substantial increase in detection accuracy. Additionally, an algorithm based on pseudo-labeling was employed to forecast pseudo-labels for defects that were not labeled, and this process was optimized to further augment the model's precision. Following these refinements, there was a 5.93% increase in detection precision relative to the baseline model. This study primarily employed fine-tuning methods, which have inherent limitations. Firstly, fine-tuning methods heavily rely on pre-trained models and a small amount of high-quality labeled data. If the labeled data are insufficient or of low quality, the model's performance may be significantly affected. Secondly, fine-tuned models may require frequent updates and retraining to adapt to ever-changing production environments or new types of defects, increasing the complexity and cost of maintenance. Future research will expand to comprehensively evaluate and compare various small-sample detection techniques. By adopting this broader approach, we aim to identify the most effective method for industrial applications, particularly in detecting steel surface defects. Additionally, this study considers it essential to develop a dataset specifically tailored for steel defect detection. Such a targeted dataset is expected to improve data quality and enhance the robustness of detection models. Conceptualization, K.A., L.J. and X.L. ; methodology, L.J. and X.L. ; formal analysis, K.A. ; investigation, K.A., L.J. and X.L. ; data curation, L.J. ; writing--original draft preparation, X.L., L.J., Y.P., D.W. and W.L. ; writing--review and editing, X.L., Y.P. and J.H. ; visualization, X.L., D.W., W.L. and Y.P. ; project administration, X.L. and K.A.