# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 4 Example Evaluation and Results
### Object Detection Results (Single Rolling Markers Characters)  
Table 2 provides a comprehensive overview of the distribution of individual classes, while Table 3 visually presents the special characters. The dataset was divided into three subsets: 80% for training data and 10% each for validation and test data. Special attention was given to ensuring that the test data included images from successive exposures, which are crucial for evaluating the subsequent analysis with the parser. The training process involved an initial training phase, which consisted of 300 epochs with a batch size of 32 and an image size of 640 \(\times\) 640 pixels. Subsequently, fine-tuning was conducted with half the batch size and a reduced learning rate until no further improvement was achieved. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Count** & & & \\ \hline
0 & 6682 (10.4\%) & L & 35 (0.1\%) & \textless{}06\textgreater{} & 3 (\textless{}0.1\%) \\ \hline
1 & 5011 (7.8\%) & M & 157 (0.2\%) & \textless{}07\textgreater{} & 5344 (8.3\%) \\ \hline
2 & 601 (0.9\%) & N & 219 (0.3\%) & \textless{}08\textgreater{} & 19 (\textless{}0.1\%) \\ \hline
3 & 173 (0.3\%) & O & 5579 (8.7\%) & \textless{}09\textgreater{} & - \\ \hline
4 & 1233 (1.9\%) & P & 15 (\textless{}0.1\%) & \textless{}10\textgreater{} & 19 (\textless{}0.1\%) \\ \hline
5 & 1503 (2.3\%) & Q & - & \textless{}11\textgreater{} & - \\ \hline
6 & 5373 (8.4\%) & R & 273 (0.4\%) & \textless{}12\textgreater{} & - \\ \hline
7 & 101 (0.2\%) & S & 491 (0.8\%) & \textless{}13\textgreater{} & 2 (\textless{}0.1\%) \\ \hline
8 & 974 (1.5\%) & T & 246 (0.4\%) & \textless{}14\textgreater{} & - \\ \hline
9 & 3092 (4.8\%) & U & 2929 (4.6\%) & \textless{}15\textgreater{} & - \\ \hline A & 312 (0.5\%) & V & 2211 (3.4\%) & \textless{}16\textgreater{} & - \\ \hline B & 83 (0.1\%) & W & 226 (0.4\%) & \textless{}17\textgreater{} & - \\ \hline C & 2985 (4.7\%) & X & 841 (1.3\%) & \textless{}18\textgreater{} & - \\ \hline D & 5235 (8.2\%) & Y & 284 (0.4\%) & \textless{}19\textgreater{} & - \\ \hline E & 2589 (4.0\%) & Z & 221 (0.3\%) & \textless{}20\textgreater{} & - \\ \hline F & - & \textless{}00\textgreater{} & - & \textless{}21\textgreater{} & - \\ \hline G & 19 (\textless{}0.1\%) & \textless{}01\textgreater{} & 184 (0.3\%) & \textless{}22\textgreater{} & - \\ \hline H & 283 (0.4\%) & \textless{}02\textgreater{} & 4 (\textless{}0.1\%) & \textless{}23\textgreater{} & - \\ \hline I & 7374 (11.5\%) & \textless{}03\textgreater{} & - & \textless{}24\textgreater{} & - \\ \hline J & - & \textless{}04\textgreater{} & 934 (1.5\%) & \textless{}25\textgreater{} & - \\ \hline K & 17 (\textless{}0.1\%) & \textless{}05\textgreater{} & 241 (0.4\%) &. & 4 (\textless{}0.1\%) \\ \hline  & & & & - & 69 (0.1\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of individual classes in the data set. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Special Character** \\ \hline \hline \textless{}01\textgreater{} & & \\ \hline \textless{}02\textgreater{} & & \\ \hline \textless{}04\textgreater{} & & \\ \hline \textless{}05\textgreater{} & & \\ \hline \textless{}06\textgreater{} & & \\ \hline \textless{}07\textgreater{} & & \\ \hline \textless{}08\textgreater{} & & \\ \hline \textless{}10\textgreater{} & & \\ \hline \textless{}13\textgreater{} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Graphical representation of the occurring special characters. in training performance could be observed. The training process made use of the hardware acceleration provided by 8 NVIDIA RTX A6000 GPUs. Figure 6 shows the obtained results based on the common evaluation metrics used in the field of image classification and object recognition, namely the mAP (mean average precision), F1-score, precision, and recall [10]. The first column shows the precision averaged over all classes at a confidence value of 0.5. The second column shows the highest F1 score. Column three shows the maximum precision.