# Tire Bubble Defect Detection Using Incremental Learning
## 3 Experimental Results  
### Dataset and Experimental Environment  
The tire speckle images used in this paper were provided by Taiwan's largest tire manufacturer. The dataset consists of tire tread and sidewall images taken from different angles. Each set contains 24 to 26 images of size 1360 \(\times\) 1024 pixels. One tire speckle image may include more than one bubble. The training and the testing dataset are independent. The training dataset contains 2450 images with 3116 bubble defects. The dataset is classified into six defect categories, as listed in Table 1. The sample tire tread images consist mostly of grooved bubbles, with 1421 images in the category, followed by butterfly-shaped bubbles. The remaining categories only account for a small part of the overall training data. The dataset consists of 1763 tread samples, containing a total of 2247 bubbles. A similar pattern exists for the sidewall images, with 321 grooved images containing 377 grooved bubbles, followed by butterfly-shaped bubbles and images from the remaining categories. There are altogether 687 sidewall samples containing 869 bubbles. Altogether, the dataset contains 2450 training images and a total of 3116 bubbles. The testing dataset is described in Table 2. Binary classification of normal and defective images is used in this study, as the primary purpose is to detect defective bubbles without ensuring the correct categorization. The test data contains 500 normal tread sample images and 810 tread images with bubble defects, as well as 500 normal sidewall sample images and 505 sidewall images with bubble defects. The experiments were performed in a Windows environment, using an Intel Core i7-8700K processor and a GeForce GTX 1080 Ti GPU with the Python Keras framework. We used four evaluation methods to verify the results of this experiment: Accuracy, Sensitivity, Specificity and Precision. The equations are as follows;  
\[Accuracy=(TP+TN)/(P+N) \tag{1}\]  
\[Sensitivity=TP/P \tag{2}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Class**} & \multicolumn{2}{c}{**Samples**} & \multicolumn{2}{c}{**Bubbles**} \\ \cline{2-5}  & **Tread** & **Sidewall** & **Tread** & **Sidewall** \\ \hline Groove & 1156 & 321 & 1421 & 377 \\ \hline Butterfly spot & 322 & 265 & 431 & 347 \\ \hline Horizontal & 91 & 22 & 134 & 49 \\ \hline Scratches & 88 & 10 & 137 & 17 \\ \hline Noise & 45 & 61 & 48 & 69 \\ \hline Not obvious & 61 & 8 & 76 & 10 \\ \hline Total & 1763 & 687 & 2247 & 869 \\ \hline Training & \multicolumn{2}{c}{2450} & \multicolumn{2}{c}{3116} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training Dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Data Class** & **\# of Image** \\ \hline \multirow{2}{*}{Tread} & Non-bubbles & 500 \\ \cline{2-3}  & Bubbles & 810 \\ \hline \multirow{2}{*}{Sidewall} & Non-bubbles & 500 \\ \cline{2-3}  & Bubbles & 505 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Testing Dataset. \[Specificity=TN/N \tag{3}\]  
\[Precision=TP/(TP+FP) \tag{4}\]  
In the above equations, \(P\) represents the total number of images containing bubble defects, \(N\) represents the total number of images without defects. \(TP\) represents the number of true positives, which are the correctly identified images with defects, and \(TN\) represents the number of true negatives, which are the correctly identified images without defects. \(FP\) represents the number of false positives, which are images without defects and incorrectly marked as defective, and \(FN\) represents the number of false negatives, which are images with defects and incorrectly marked as normal. As the purpose of quality control in tire manufacturing is to detect all bubble defects, we use sensitivity as the main evaluation standard for this paper since false positives have a lesser effect compared to false negatives.