## 4 Machine Learning-Based Label Quality Assurance
### Neural Network Architecture for Machine Learning-Based Label Quality Assurance  
The neural network is designed to accept both the image and its corresponding label, performing classification to determine if the label is good (outputting 1) or bad (outputting 0). All images are resized to 640 \(\times\) 640, maintaining their aspect ratio, as the maximum width and height in the training set are both 640. Black padding is added if the width and/or height are smaller. Labels are represented as 640 \(\times\) 640 images with 80 channels, where pixels representing the border of the labeled bounding box are set to 1 and all other pixels are set to 0. For the padded image in Figure 14, the label representation is shown in Figure 15 (displaying only the image planes corresponding to the classes present in the image: class \"sink\" and class \"bottle\"; the remaining 78 image planes are black).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**A1** & erase one or multiple label(s) from uncertainty regions \\ \hline
**A2** & distort label that is inside uncertainty region to become outside of uncertainty region \\ \hline
**A3** & swap classes of labels from uncertainty regions \\ \hline
**B1** & add new labels to ground truth classes, outside of any uncertainty region \\ \hline
**B2** & add new labels to classes that do not belong to any ground truth classes, outside of any uncertainty region \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error subtypes with descriptions.  
Figure 13: Error types B.  
Figure 16 illustrates the neural network architecture being utilized.  
Figure 14: Padded image that goes to the label quality assurance neural network.  
Figure 15: Representation of the labels for objects “sink” and “bottle” that goes to the label quality assurance neural network. Other planes are all black because there are no other MS COCO classes annotated for this particular image.  
In this study, a neural network is employed as the feature extractor. The images are input into the neural network in their original format, while the labels are provided using the aforementioned label representation method. It is worth noting that some studies suggest that incorporating wavelet transform-based feature extraction techniques could potentially enhance the accuracy of neural networks [41]. This is an area that warrants further exploration in future research. Additionally, there are a few studies that discuss the integration of wavelet transforms and neural networks, which presents promising avenues for improving the effectiveness of neural networks in machine learning-based label quality assurance tasks [42; 43]. Furthermore, alternative traditional machine learning algorithms, such as support vector machines (SVMs), random forests and others could also be employed for this task. Feature extraction techniques, such as gray-level co-occurrence matrix (GLCM), can be utilized in conjunction with these algorithms to further improve classification performance.  
Figure 16: Label quality assurance neural network architecture. The ellipsis displayed on the image signifies the existence of 80 channels, which are not visually presented. “NN” stands for “Neural Network”.