High-Precision Image Segmentation for Computer Vision
Managed end-to-end image segmentation workflows within Labelbox for a large-scale Obstacle Detection project. My primary responsibility involved pixel-level semantic and instance segmentation for a dataset of over 6,500 high-complexity images. I utilized polygons, brushes, and superpixel tools to define precise boundaries for a wide range of obstacles, including pedestrians, cyclists, static debris, and various vehicle classes. I focused on maintaining extreme spatial accuracy, particularly for occluded objects and edge cases in diverse weather conditions. My work directly contributed to improving the model's 'mean Intersection over Union' (mIoU) scores. I consistently exceeded quality benchmarks, maintaining a 98%+ accuracy rating while adhering to a strict class ontology