This study focuses on enhancing impurity detection in laver images through advanced data augmentation and tailored dataset design. The irregular texture of laver and the microscopic size of impurities present significant challenges for traditional detection methods. To address these issues, a comprehensive dataset was constructed using StyleGAN to generate 10,000 synthetic laver images, coupled with traditional augmentation techniques for impurity data. The YOLOv8 model was employed for impurity detection, achieving a precision score of 0.96 under IoU 75, a substantial improvement from the 30
Recently, 3D data generation techniques using neural networks are widely used in a variety of fields. In our study, we propose an efficient interactive annotation system by combining two 3D models: a high-quality 3D scene model generated by 3D Gaussian splatting and a model with surface information such as a mesh model. The system allows users to annotate the 3D space with intuitive operations. This system is expected to promote the effective use of 3D data and information sharing, and to be deployed in a wide range of scenes such as living environments.
Personal Protective Equipment (PPE) detection is essential for strengthening intelligent surveillance systems and ensuring worker compliance with safety protocols in modern manufacturing facilities. A YOLO-based PPE detection model provides an efficient solution for resource-limited environments, supporting real-time operation at high speeds. This work proposes a Multi-Perspective Attention (MPA) module to enhance YOLO11n’s performance in PPE detection, enabling its feature extractors to focus on critical elements. Hence, the improved YOLO11n achieves superior results compared to other methods on the PPE Detection (PPED) and Color Helmet and Vest (CHV) datasets, operating at 19.94 and 28.09 frames per second (FPS) on an Intel Core i7-9750H CPU and NVIDIA Jetson Orin Nano GPU devices, respectively, without significantly increasing parameters or computational cost.
This paper describes a method for creating human action sentences from video information, which aims to infer the purpose of human actions from video information. To develop a flexible human-robot communication method that enables robots to infer their own roles from the appearance of people, it is necessary to observe and convert human behavior into data. Therefore, we have created sentences describing human behavior by combining an action recognition model and an object detection algorithm, and we have organized the spatial relationships between objects and people. In Verification 1, a simple one-sentence explanation of the behavior was provided based on the distance between the two-dimensional (2D) coordinates of the person and the object taking the action from the video image information. Through the verification, we have identified issues in the generation of behavioral sentences. In Verification 2, we spatially organized their relationship based on the change in the three-dimensional (3D) coordinates of the object and the person. Through this validation, we were able to obtain spatial information on human behavior and object access records. In the future, by integrating these methods, we aim to generate an in-spatial action log that links human behavior and spatial information.
Human fall detection has been studied and applied in health care and human monitoring systems. Falls often occur in the elderly, and people with neurological and musculoskeletal diseases. It can also appear in heavy work environments, slippery surfaces, or human carelessness. Falls cause many serious health injuries, especially to the spine and brain. Developing tools to warn of falls early is essential and reduces many unnecessary risks. This paper proposes vision-based human fall detection and warning systems using person detection pre-trained models. Thereby, evaluating and comparing the performance between three nano versions of the YOLO (You Only Look Once) architecture, including YOLOv5n, YOLOv8n, and YOLOv11n. The YOLOv8n model achieved the best speed at 109.06 fps (FPS) on an NVIDIA GeForce GTX 1080Ti 11GB GPU as a real-time test result. The demo video can be accessed at this link: https://bit.ly/4flU7Xv .
