Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object and preprocessing for further analysis. Connecting specialists sharing specific ideas is most effective using the native language. The main objective of this paper to analyze propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, procedures with simplified methods achieve reasonable accuracy textual development conference translation based on artificial intelligence supported solutions has a relevant impact in health sector, especially clinical practice via better consultation (VC) or diagnosis. importance was augmented by COVID-19 pandemic. challenge topic connected variety languages dialects that involved speak usually needs human translator proxies which can be substituted AI-enabled technological pipelines. sensitivity element localization directly complexity, quality, collected training data sets. In research, we investigated DETR model several variations. research highlights differences prominent detectors: YOLO4, DETR, Detectron2, brings AI-based novelty collaborative combined OCR. performance evaluated through two phases: 248/512 (Phase1/Phase2) record train set, 55/110 set validated instances 7/10 application categories 3/3 categories, same annotation. achieved score breaks expected values terms text scope, giving high data, mean average precision ranging from 0.4 0.65.