Welding Image Defect Annotation (Machine Learning Project Intern)
OtherImageObject Detection
I participated in a project focused on labeling welding defect images using machine learning and computer vision techniques. My role involved detecting objects and classifying defects in Regions of Interest within weld images for AI model training. I worked on creating comprehensive annotations to assist model accuracy and evaluation.
• Implemented YOLO for ROI detection and used CNNs for defect classification.
• Used image preprocessing (Gaussian blur, edge detection, morphological transforms) to enhance image quality before annotation.
• Interacted with data labeling and defect identification systems via a Python/PyQt5 GUI.
• Collaborated closely with team members to ensure data quality and consistency in annotation tasks.
I participated in a project focused on labeling welding defect images using machine learning and computer vision techniques. My role involved detecting objects and classifying defects in Regions of Interest within weld images for AI model training. I worked on creating comprehensive annotations to assist model accuracy and evaluation. • Implemented YOLO for ROI detection and used CNNs for defect classification. • Used image preprocessing (Gaussian blur, edge detection, morphological transforms) to enhance image quality before annotation. • Interacted with data labeling and defect identification systems via a Python/PyQt5 GUI. • Collaborated closely with team members to ensure data quality and consistency in annotation tasks.
2024 - Present