## 6 Conclusions  
This paper presents a high-precision defect detection model based on YOLOv5, specifically tailored for resource-constrained devices. A robust and efficient model, GLDD-YOLOv5, is proposed by integrating an ECA mechanism to increase the feature extraction capacity of the backbone network, Ghost modules to lighten the model architecture, and a P34 head structure to increase the detection speed. By replacing the backbone of the YOLOv5 network with both Ghost modules and ECA mechanisms, feature extraction capabilities were improved without compromising detection accuracy, and small to medium-sized defects in images were targeted. Adjustments to the model's head structure further concentrated detection on small and medium-sized areas, effectively balancing detection accuracy and extraction speed. The contribution of each module has been demonstrated through ablation studies, showing the impact of each modification on model performance. The proposed model outperforms the other six YOLO family versions, achieving a remarkable balance of performance with an average detection accuracy of 80.6% ([EMAIL_ADDRESS]), computational efficiency of 8.4 GFLOPs, 3.96 M parameters, and memory footprint of 10.7 MB. In terms of real-time performance, the model achieved 163.9 FPS on RTX2080 Ti and produced a total carbon footprint of 0.549 \(\times\) 10\({}^{-3}\) gCO\({}_{2}\)e, demonstrating its environmental friendliness as well as its speed of inference compared to other models.  
Considering these strengths, GLDD-YOLOv5 stands out as an important solution for LPG cylinder surface defect detection. Its high detection accuracy, low computational cost, adaptability to constrained environments, and environmental friendliness make it a suitable candidate for use in real-world and defect detection applications.  
This study is limited to finding defects on the surface of LPG cylinder surfaces, and hence their location. The location of the defect on the surface, the type of defect, and the shape of the defect were not studied. In future studies, with expansion of the dataset, a different classification will be created by determining the type and location of the defect. In addition, it is planned to evaluate the performance of the proposed model by applying it to inexpensive edge devices such as Raspberry Pi 5 and Nvidia Jetson Nano. Future research could focus on further development of this architecture, potentially increasing its applicability to a wider range of surface defect detection scenarios in various industrial applications.  
This research was funded by AYGAZ A.S., no grant number was assigned.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: Restrictions apply to the availability of these data. Data were obtained from funders and are available from the authors with the permission of the funders.  
The author deeply appreciates the team at AYGAZ A.S. for their essential contribution in gathering the images that were used to create the dataset for this study.  
The author declares no conflicts of interest. The funder had no role in the design of the study, analyses, interpretation of data, writing of the manuscript or decision to publish the results.