The manipulation of deformable objects, particularly garments, remains a significant challenge in robotics due to their infinite-dimensional state space and unpredictable dynamics. This thesis addresses the critical need for robust perception systems by designing, implementing, and evaluating an end-to-end vision pipeline for autonomous robotic handling of clothing. The research is contextualized within the VolPix project from EUROBIN, which targets the automation of laundry tasks involving ten distinct garment categories in both wet and dry states. The proposed system employs a multi-stage approach to transform a single RGB image into actionable data for a robotic manipulator. The pipeline begins with instance segmentation to isolate individual garments from cluttered scenes, followed by object recognition to determine each item's category.