Bridging AI research with real-world impact, from active industry collaborations to completed systems.
The project aims to develop an intelligent bin-picking system that enables robots to detect, localize, and grasp objects in cluttered, unstructured environments. By combining advanced vision techniques such as instance segmentation, graph-transformer reasoning, depth refinement, shape completion, and 6D pose estimation, the project seeks to overcome challenges like occlusion and sensor noise.
MPTC AI-Driven Administration is a collaborative initiative between the Ministry of Post and Telecommunications of Cambodia (MPTC) and CBNU's AICLab. The project applies LLM technologies to address operational inefficiencies in government administration. Its goal is to develop AI-driven tools that streamline workflows, improve information processing, and enhance organizational efficiency, ultimately supporting faster and more consistent public service delivery.
The project aims to develop an AI-assisted module development system that partially automated COOCON’s scraping module creation process. Leveraging artificial intelligence, network traffic analysis, and browser automation technologies, the proposed system aims to reduce manual effort, minimize human error, and accelerate the overall development lifecycle of COOCON developer.
The project aims to develop a real-time fall detection system using YOLO for high-speed human pose estimation to extract skeletal keypoints from video streams. Through feature engineering, it computes a dozen biomechanical angles capturing body orientation and velocity, which are then fed into a classification-based model to accurately distinguish intentional movements from sudden, unintentional falls.
The project aims to enhance motorcycle rider safety and aid law enforcement in Vietnam by automatically detecting riders without helmets using surveillance camera footage. The proposed method uses the YOLOv5 object detection algorithm, a state-of-the-art model custom-trained on a local dataset to detect three specific classes: "Helmet", "No-helmet", and "Rider". A critical post-processing stage is then applied to analyze the relationships between these detected objects.
The project aims to develop an automated surveillance system to enhance public safety by detecting abandoned objects (specifically luggage and paper boxes) in real-time. The system is designed to minimize security risks and false alarms by distinguishing between attended and unattended items using CCTV footage. The core of the system is a custom-trained YOLOv8 model, which performs initial object detection to identify "persons", "luggage", and "paper boxes". This is integrated with the ByteTrack algorithm to assign unique IDs and track the movement of these objects across consecutive frames.
