In this project, students will collaborate to enhance the quality and diversity of the image dataset for material identification. They will apply data augmentation techniques to create variations of the existing images. These augmented images will be crucial for training robust machine learning models capable of accurate visual material classification.

Project Description:

In this project, students will work together to improve the image dataset's quality and diversity by applying data augmentation techniques. The project consists of the following key tasks:

Image Dataset Review:

• Students will review the existing image dataset containing photographs of different materials (resin, dyed materials, natural minerals, and rocks).

Gain an understanding of the current dataset's strengths and areas where it can be improved.

Data Augmentation Techniques:

• Students will learn about various data augmentation techniques commonly used in computer vision, such as rotation, scaling, flipping, cropping, brightness adjustment, and noise addition.
• Understand how each technique can create variations of the original images.

Data Augmentation Implementation:

• Apply data augmentation techniques to the existing images to create augmented versions.
• Use relevant image processing libraries or tools (e.g., Python's OpenCV) to implement these techniques effectively.

Quality Control:

• Ensure that the augmented images maintain their accuracy and represent the original materials faithfully.
• Review the augmented dataset to identify any anomalies or issues.

Project Deliverables

Upon completion of the project, students will deliver the following:

• Augmented Image Dataset: A dataset containing the original images along with their augmented versions, effectively increasing the dataset's size and diversity.
• Documentation: A report summarizing the applied data augmentation techniques, any challenges encountered, and how quality control was maintained.

Support for Learners:

To ensure that learners successfully complete this project and achieve the desired learning outcomes, the following support mechanisms will be provided:

1. Guidance and Training: Students will receive guidance on image augmentation techniques, and resources such as tutorials and documentation will be made available.
2. Regular Check-Ins: Periodic check-in sessions with project mentors or instructors will allow students to seek guidance and feedback.
3. Access to Software: Access to relevant software tools and libraries for image manipulation will be provided.
4. Quality Control Guidelines: Clear guidelines on maintaining image quality during augmentation will be shared with students.
5. Collaborative Environment: Students will have the opportunity to collaborate with peers, share insights, and discuss challenges related to image augmentation.

By offering these forms of support, learners will be well-equipped to complete the project successfully, enhancing their data manipulation skills and contributing to the development of a robust material identification system.