The paper addresses the problem of improving the accuracy of automated object detection in thermal images under conditions of low contrast, sensor noise, and structural uncertainty of the terrain. The relevance of this research is driven by the increasing use of unmanned aerial vehicles for monitoring and reconnaissance, where thermal imaging systems serve as a key source of information in low-visibility environments.
This study explores the application of YOLOv8, a cutting-edge object detection framework, to detect underwater waste. As concerns about protecting marine ecosystems grow, the effective identification and removal of underwater debris have become critical. The research adapts YOLOv8 to address challenges such as variable lighting, distortions, and occlusions in underwater environments. A specialized dataset, containing various underwater landscapes and debris types, was created to train and evaluate the model. Experimental results show strong performance across differ
Resume parsing is a method used to extract key information from resumes, allowing for further actions such as candidate selection and ranking. In traditional recruitment processes, companies often handle thousands of resumes manually or require applicants to follow a pre-defined template. However, the evolving recruitment environment calls for more advanced technological solutions and efficient resume analysis methods. Although various basic techniques can analyze structured documents, they are inadequate for processing unstructured formats such as PDF, DOC, and DOCX
This paper presents an approach to improving mobile robot localization by filtering dynamic objects using camera image segmentation. The proposed algorithm integrates a Particle Filter with state-of-the-art computer vision techniques, specifically employing the YOLO model for segmentation, which effectively differentiates static elements of the environment from moving objects. This approach reduces the impact of noisy data and enhances localization accuracy in dynamic conditions, which is crucial for the reliable autonomous operation of mobile robots.
The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m, and YOLO models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, Python, and roboflow 3.0 FAST, the research aims to develop a robust methodology for assessing the performance of these NN models.
The objective of this research is to conduct a comprehensive performance analysis of various types of neural network (NN) models for target recognition. Specifically, this study focuses on evaluating the effectiveness and efficiency of yolov8n, yolov8s, yolov8m models in target recognition tasks. Leveraging cutting-edge technologies such as OpenCV, the research is aimed at developing a robust methodology for assessing the performance of these NN models.