Hengyao C.

Key Skills

Software

Data Annotation Tech

Don't disclose

Top Subject Matter

Autonomous Driving

Street Scenes

Vehicle Recognition

Top Data Types

Image

Text

Document

Top Task Types

Object Detection

Bounding Box

Segmentation

Classification

Point/Key Point

Polyline

Cuboid

Text Generation

Question Answering

Text Summarization

Evaluation/Rating

Data Collection

Freelancer Overview

Image Object Detection Annotation for Autonomous Driving. Core strengths include LabelImg. AI-training focus includes data types such as Image and labeling workflows including Object Detection and Bounding Box.

Labeling Experience

Image Annotation for Autonomous Driving Vehicle Recognition

LabelImgImageBounding Box

I performed 2D bounding box and semantic segmentation annotation for vehicles, pedestrians, and traffic lights in images related to autonomous driving. I established best-practice rules for visible-part annotation to optimize label utility for downstream AI models. I consistently maintained high efficiency, regularly producing high volumes of quality-labeled data with minimal rework required. • Completed over 300 annotation boxes per day with high accuracy. • Applied best-practice guidelines for bounding box and semantic segmentation annotation. • Contributed to reduced false positives in model training through refined labeling rules. • Used LabelImg and related tools to complete data labeling tasks efficiently.

Not specified

Image Object Detection Annotation for Autonomous Driving

LabelImgImageObject Detection

I annotated over 2,000 street scene images, focusing on vehicles, pedestrians, and traffic lights for object detection tasks. I proactively developed annotation guidelines to address occlusion, which improved team consistency and label quality. I adhered to strict annotation accuracy standards and utilized specialized software to track and maintain progress. • Performed bounding box labeling using LabelImg for vehicle and pedestrian detection. • Summarized and shared occlusion annotation guidelines with the team. • Increased team annotation consistency from 82% to 94% through improved guidelines. • Used Excel for annotation progress tracking and quality assurance.

Not specified

Education

Work History