byeol3325 byeol3325

Hi 👋, I'm SungHo Moon

I plan to continue uploading the projects I have done in the future.
🌱 I’m currently learning multi-modal, multi-camera, lidar.
👯 I’m looking to collaborate on everything about computer vision.
💬 Ask me about everything about me.
📫 How to reach me [email protected] or [email protected].
🙊 my github blog git blog.
👇 I have experience collaborating with companies here !!! About 2D/3D Object Detection, 3D Reconstruction(Bundle Adjustment, Pose Graph Optimization), On-board(memory & time optimization), Camera Calibration(using 1D/2D chessboard), AI ethics, AI module performance improvement in specific environment ...

Brief description of the projects involved 📚

Goal: Develop a 3D reconstruction module using monocular images.
Role: Lead Researcher (80% contribution) – Designed and implemented keypoint matching (SIFT/SURF), computed epipolar geometry, estimated camera relationships, and applied PnP & bundle adjustment (BA). Led the development of the full 3D reconstruction pipeline.
Achievement: Successfully built a 3D reconstruction module that processes monocular images to generate 3D structures.

Goal: Improve the robustness of monocular camera-based 3D object detection, addressing significant performance degradation caused by varying camera environments.
Role: Lead Researcher (70% contribution) – Developed data augmentation techniques to enhance model generalization across different camera angles, identified the root causes of performance degradation, and implemented correction algorithms to mitigate these effects.
Achievement: Diagnosed key factors affecting model accuracy and significantly improved performance:
- Accuracy increased from 20% to 80% for a 3-degree angle variation.
- Accuracy increased from 1% to 50% for a 5-degree angle variation.
- Research findings contributed to international patents and publications(CVPRw 2024).

Goal: Develop a module capable of estimating vehicle position and speed solely from CCTV video data.
Role: Lead Researcher (80% contribution) – Engineered road detection and image warping algorithms, developed vehicle speed estimation methods, and optimized overall system performance.
Achievement: Achieved over 90% accuracy in vehicle speed estimation on the target dataset.

AI 시험평가 연구원 (30% contribution)
Goal : Develop new testing and evaluation standards for AI weapon systems, which differ significantly from traditional weapon systems.
Role : As an AI Test and Evaluation Researcher, collaborated with the U.S. Department of Defense, coordinated with the Ministry of National Defense, and conducted extensive research on AI weapon systems, including identifying requirements (Contribution 30%).
Achievements : Established initial standards for the Military Performance Certification Center (including dataset construction, baseline model development, and formulation of various strategies).

Lead Researcher (60% contribution)
Goal : When introducing various AI weapon systems in the Army, create a military learning/test data set and build an AI model that serves as a standard.
Role : Developed an Auto-Labeler for military datasets, performed data cleansing and construction, and developed a baseline model for performance verification (Contribution 60%).
Achievements : Established initial standards for the Military Performance Certification Center(including dataset construction, baseline model development, and formulation of various strategies).

Lead Researcher (80% contribution)
목표 :
역할 : 추가적인 2D 알약 검출 알고리즘 학습없이 알약을 검출하고 어떠한 알약인지 인식하는 알고리즘 개발(Template matching, color 고려, warping 등)
성과 :

목표 :
역할 : (핵심 개발자, 40% contribution)text -> 3D model, 3D model + text => 수정된 3D model
성과 :