Developed automatic and manual labeling pipeline for detection and segmentation datasets. This project demonstrates practical execution from architecture and implementation to measurable delivery outcomes.
Objective
Developed automatic and manual labeling pipeline for detection and segmentation datasets.
Stack
Delivery highlights
Extended previous Electric Vehicle Charger Socket Instance Segmentation and Electric Vehicle Charger Socket Instance Segmentation & Tracking projects by building a custom DAVIS-style dataset with YOLOv8-Seg and adapting it for XMem to enable temporally consistent pixel-level segmentation across video frames.
Built upon the previous Semi-Supervised Video Object Segmentation for Electric Vehicle Charger Socket Tracking project by applying small-object-focused data augmentation to address missed detections when charger sockets appeared smaller or farther from the camera. This improved the model’s ability to detect small and hard-to-detect sockets while reducing missed detections in real-world scenarios.
Built detection plus tracking pipeline for EV sockets with persistent IDs.