## 5 Conclusions  
Aiming at the problems of low efficiency of manual eye inspection and large subjective error in the concentricity detection of piston components, an improved Canny algorithm is proposed in this paper, which realizes high-precision automatic measurement through bilateral filtering, multi-scale analysis, and sub-pixel positioning optimization. The improved Canny algorithm is applied to the first piston component spring concentricity detection, and the results are compared with those of CMM. The main conclusions are as follows:  
1. Algorithm improvement: The two-sided filter is applied to the Canny algorithm to solve the problem of image edge information loss during image preprocessing. Multi-scale analysis is introduced, an image pyramid is constructed, and the problem of detail loss caused by the Sobel operator in the Canny algorithm in a single-scale edge detection is solved, and thus the edge detection accuracy is improved. 2. Measurement performance: The concentricity detection error in the laboratory environment is less than \(\pm\)0.01 mm, which meets the ISO 1938-2015 standard [20]. The deviation is reduced by 4%, and the repeatability is significantly better than manual detection. 3. Application potential: The method is compatible with the existing structure of the riveting machine testing station and has the engineering conditions of non-contact real-time measurement. It provides a reliable technical solution to replace manual eye inspection. The next step will be to carry out the adaptation verification of the production line. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fitting Times** & **Original Image 1** & **Original Image 2** & **Original Image 3** & **Original Image 4** & **Original Image 5** \\ \hline First time & 0.61872 & 0.66529 & 0.70329 & 0.63603 & 0.64464 \\ Second time & 0.78596 & 0.50514 & 0.65660 & 0.61228 & 0.59718 \\ Third time & 0.60205 & 0.64223 & 0.65253 & 0.61342 & 0.63857 \\ Fourth time & 0.72609 & 0.60871 & 0.63906 & 0.64046 & 0.40322 \\ Fifth time & 0.61912 & 0.64333 & 0.57418 & 0.61936 & 0.64323 \\ Sixth time & 0.66031 & 0.69923 & 0.60923 & 0.64542 & 0.65148 \\ Seventh time & 0.61843 & 0.60355 & 0.74016 & 0.61078 & 0.50514 \\ Eighth time & 0.66968 & 0.66274 & 0.63218 & 0.62800 & 0.89484 \\ Ninth time & 0.48028 & 0.71846 & 0.65326 & 0.76218 & 0.81706 \\ Tenth time & 0.65196 & 0.70728 & 0.68641 & 0.72281 & 0.65236 \\ Average of ten times & 0.64326 & 0.6456 & 0.65469 & 0.64907 & 0.64477 \\ Overall average & & & 0.6475 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitting data (unit: mm). **Author Contributions:** [PERSON] conceived the experiments; [PERSON] and [PERSON] performed the experiments; and [PERSON] and [PERSON] analyzed the results. All authors have read and agreed to the published version of the manuscript. **Funding:** We need to thank the following organization for its strong support: the Natural Science Foundation of Jilin Province--General Project, 20220201043 GX. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data on spring concentricity detection of automobile first piston parts in this study mainly come from experimental measurements. In the experimental process, we used the improved Canny algorithm for detection and compared the results with those measured by CMM and the traditional Canny algorithm. These experimental data were obtained under strictly controlled experimental conditions to ensure the accuracy and reliability of the data. Since the experimental data relate to a specific automotive brake piston component inspection study, the disclosure of some of the data may be limited by the agreements of the relevant partners. However, upon reasonable request and in compliance with relevant regulations, we will consider providing aggregated data results to support the reproducibility of the results of this research and further scientific exploration.