## 3 Proposed Process for Data Requirements within Requirements Engineering
A representative subset of the collected data should be assessed, and if significant issues are found, the process may be repeated for all or affected images. Decisions should consider the project's needs, resources and timeline. The next step is to label the data according to the defined classes. Labels should correspond to the expected output of the trained model, and in some cases, expert knowledge may be necessary. The amount of labeled data needed for training a machine learning model on image data depends on the variance in factors such as content, lighting, image quality, backgrounds and camera angles. High-variance image datasets require more labeled data to help the model generalize better, while low-variance datasets with simpler patterns may need less labeled data to achieve good performance. The labels quality assurance activity follows, during which the quality and distribution of labels (proportions of different objects or classes within the labeled dataset, as well as the spatial distribution of these objects in the images) are checked. If the quality is bad, the data labeling activity should be repeated. This work further elaborates on what constitutes good or bad label quality in Section 4.3. If there are many outliers in the labels and the reason is related to the data content, it may be necessary to return to the production-like image data acquisition activity. The final product is computer vision software that generates predictions, such as a cat detector system, face recognition system or other similar applications. There are certain assumptions about the project. Although this work focuses on detection, recognition and segmentation tasks in computer vision and presents a machine learning-based label quality assurance method for object detection tasks, the approach can be extended to other computer vision tasks and other machine learning domains, ranging from natural language processing to tabular data analysis. The software can be cloud-based. Security, privacy, types of cloud resources, cost and other aspects are not discussed in this work, as it is assumed that industry standards are followed. The response time of the system is also not discussed, but it is assumed to be reasonable--not in real-time, but not taking several minutes either. A response time in the range of seconds is expected. Some limitations are present in the proposed data requirements process. Over-regulation can be an issue, as highly regulated data for solving specific tasks can potentially halt the project. Moreover, if there is high variance in a large dataset or a multitude of different labels, creating a model with the current state of technology may be impossible (e.g., a general object detection model). In the following section, a novel machine learning-based label quality assurance method is presented. It uses a small amount of carefully annotated data to build a model that can solve the binary classification task of estimating whether a label for a given image is of good or bad quality. By automating the process of determining label quality, the need for manual inspection and validation of labels is reduced, saving time and resources.