# Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
## 6 Conclusions  
The paper proposed DETR for visual, textual data recognition to support future translation of multiuser videoconferences and to boost the efficacy of distance diagnosis. The Textual and VOD project analyzed different scenarios to provide reliable outcomes for a new collaborative working model. Results will be applied to various sectors (e.g., health, sports) that use remote consultations or e-collaboration tools and videoconference platforms (like Zoom, Google Meet, Microsoft Teams, Skype, Google Hangouts, GoToMeeting, Cisco Webex Meetings, Bluejeans, Intermedia Anymeeting, RingCentral Video, Zoho Meeting, Dialpad Meetings, TrueConf Online, Lifesize Go or even Youtube Live, Facebook Live, Slack Video Calls, etc.). The research validated, the feasibility of the Textual Object Detection topic, using several different models and architectures to support video diagnosis using conference tools. The main goal of the investigation was to run some limited validation experiment rounds on each candidate group, summarize their results, compare the outcomes, and bring specific requirements about the input data and the preparation process, and the efficacy of the new approach. In the case of multilingual conferences, to support further data processing, such as neural multilingual translation, a feasible visual (textual) object detection model can be prepared by a modest number of specific training datasets using the DETR model. When it comes to adjusting the efficacy level, it depends on clustering or object classes, quality- and quantity of training data. The solution provides satisfactory results, starting from 0.50 mAP for this use case, which is an acceptable outcome connected to data science and DL projects. Using DETR with the clustering approach for textual object detection, the prediction of bounding boxes give high accuracy at approximately 500 epochs (see Figures 5 and 6), where all experiments reach their peak performance. The results of mAP = 0.65 represent high accuracy in textual object detection, as shown in Figure 9, being promising from the collaborative, multilingual solutions perspective. It could bring a new line of development for specific applications. In VOD projects, we usually expect mAP values above 0.9 to prove the feasibility of different use cases. However, if we are only looking for texts and phrases, the expected criteria are already met above 0.5 mAP. Experiments have demonstrated that using the DETR model for a restricted number of training datasets with real-time collaboration tools is possible to strengthen multilingual collaboration with multilingual translation. The textual data object detection is feasible having a mean average precision of 0.65. **Author Contributions:** Conceptualization, A.B., S.M.S. and A.I.C.-V.; methodology, A.B., A.I.C.-V., J.M.-M. and S.M.S. ; software, A.B. and K.T.J.-R.; validation, A.B., K.T.J.-R., L.S. and S.M.S. ; formal analysis, A.B., J.M.-M. and S.M.S. ; investigation, A.B. and K.T.J.-R.; resources, A.B. and S.M.S. ; data curation, A.B. and K.T.J.-R.; writing--original draft preparation, A.B. ; writing--review and editing, A.B. and L.S. ; visualization, A.B. and L.S. ; supervision, A.I.C.-V., J.M.-M. and S.M.S. ; project administration, A.B. ; funding acquisition, A.B. and L.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by ITware, Hungary. The work of [PERSON] and [PERSON] was supported by Sapientia Foundation--Institute for Scientific Research. **Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are available upon request. The Figures (DETR architecture, Detailed DETR transformer architecture, YOLO4 architecture, Detectron detailed architecture) and the results charts of this study (Figures 3-6, DETR OCR output) can be found in: [[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)). 6084/m9.figshare.19699993 (accessed on 31 May 2022).