# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 5 Conclusions  
This study proposes a robust defect detection method that combines the SIFT algorithm with traditional image similarity evaluation techniques such as SSIM, PSNR, and MSE, enabling reliable quality inspection despite product rotation and scale changes. By detecting key points in the image and correcting for rotation and scale using the SIFT algorithm, the system demonstrated the capability for accurate quality control. Additionally, the introduction of the Scombined metric allowed for precise defect analysis by leveraging the strengths of SSIM, PSNR, and MSE, offering flexible responses to various defect scenarios. In the load-cell-based counting system, the YOLOv8 Pose algorithm was employed to correct counting errors in real time when a person was on the load cell. Furthermore, the image differencing technique was used to calculate unit weight, enabling accurate product counting. Experimental results showed a high accuracy of 99.268% for products weighing between 1 kg and 45 kg. In conclusion, this research demonstrated that reliable defect detection can be achieved despite rotation and scale changes using the SIFT algorithm, and accurate quality inspection and counting systems can be implemented in manufacturing processes by utilizing the image differencing technique and YOLOv8. This confirmed that high reliability and accuracy can be maintained even in real-time manufacturing environments.  
Future research should focus on improving the processing speed of the SIFT algorithm and optimizing the system to maintain stable performance in conditions with lighting changes or complex backgrounds. In particular, efforts should be made to simplify the algorithm for enhanced real-time performance and to integrate machine-learning-based predictive models for advanced automation in defect detection. Additionally, for the load-cell-based counting system, it is essential to introduce technologies that enhance resolution or develop methods capable of detecting smaller weight changes with greater precision. Algorithms that can correct counting errors in real time should be advanced, and the system's stability must be reinforced to remain unaffected by external environmental factors such as temperature and vibration. These advancements are expected to play a crucial role in enhancing the efficiency of quality inspection and counting processes in automated manufacturing systems.  
**Author Contributions:** Conceptualization, C.L. and Y.K.; methodology, C.L. and H.K.; software, C.L.; validation, C.L. and Y.K.; formal analysis, C.L. and Y.K.; investigation, C.L. and Y.K.; writing--original draft preparation, C.L. and Y.K.; writing--review and editing, H.K.; project administration, C.L.; visualization, C.L.; supervision, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2024-RS-2023-00259678).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.