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    Two-Tier AI Surveillance: Enhancing Weapon Detection Through Edge and Cloud Collaboration
    (2026-01-01)
    Sun, Sanchai
    ;
    Songsuwankit, Kanoknuch
    ;
    Thamrongrongveerachart, Chawapon
    ;
    Yanyong, Sarucha
    ;
    Sri-on, Jiramate
    This research presents a novel two-tier AI-based surveillance system designed for real-time weapon detection to enhance security and prevent potential robberies. The system leverages the computational capabilities of both edge and cloud resources, integrating a YOLOv5s model on a Jetson Nano for initial detection and a YOLOv8l model on a server equipped with an Nvidia A100 for refined analysis. The initial detection performed by the Jetson Nano rapidly identifies potential threats and forwards compressed images to the server, optimizing bandwidth usage and transmission speed. Upon receipt, the server applies image enhancement techniques to restore and upscale the images from 640 pixels back to 1280 pixels before further verification. Utilizing Python Django, the server processes the enhanced images with a more sophisticated model to ensure high accuracy in detection. The integration of edge computing optimizes the system’s performance by enabling real-time processing and reducing latency. This hybrid approach enhances the efficiency and scalability of the surveillance system, ensuring robust and timely detection. Upon confirming the presence of a weapon, the system sends immediate alerts via LINE Notify to both users and local law enforcement, thereby enabling prompt responses to potential security threats. Additionally, the Jetson Nano hosts a Flask application, allowing users to download previously recorded videos for further review and evidence collection. The proposed solution combines edge computing, cloud processing, and image optimization techniques to provide an efficient, scalable, and high-performance solution for real-time weapon detection which enhancing accuracy and reliability in real-world surveillance.
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    Retrieval-Augmented Large Language Model Implementation for Context-Aware Robotic Task Execution
    (2026-01-01)
    Kanjanarajit, Teeratorn
    ;
    Somsak, Thanapoom
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    Suttitanon, Wachirawut
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    Chimsakul, Parn
    ;
    Aphiratsakun, Narong
    With the expanded use of robot technology in everyday lives, there is a need for a convenient and flexible method to control robots. With this work, we seek to implement a system that uses AI to interpret natural language to issue commands to our mobile robot based on given context and environment. In this work, we have implemented a system using AI to interpret natural language to issue commands to our mobile robot while reflecting the accuracy and practicality issues.
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    On-edge 2D-to-3D generative pipeline for seamless instance transformation
    (2025-12-01)
    Petchhan, Jirayu
    ;
    Doungtap, Surasachai
    Despite ongoing challenges with fragmented workflows, latency in device imports, and the main issue of limitations in object reconstruction functionality, relying on imperfect extraction networks remains an impractical solution for scalable object generation. To deal with these constraints, we proposed an end-to-end pipeline that leverages a re-designed self-consistency mechanism—aimed at reducing discrimination, along with the beneficial enhancement from level-set projection and gradient-surface orthogonality. In addition, our approach designs dynamic 3D object creation with minimal manual effort by unifying surface topology and optimizing data loading, enabling a streamlined reconstruction process and more flexible object projection. Our method supports rapid, resource-efficient mesh reconstruction and consistently demonstrates performance improvements across multiple instance benchmarks, covering virtual projection tasks. Improvements in mesh topology reconstruction, as measured by the L1 Chamfer distance (CD) metric, are consistently higher, while the system also achieves significant transmission speedups—up to 56.5×—near-instant importing—along with lowering latency in practical rendering on virtual reality (VR) devices. This result highlights that refining mesh binding improves re-creation fidelity. Our approach to scalability leads to faster user engagement and allows automated deployment without requiring human intervention during importing.