Image target detection and recognition had been widely used in many fields. However, the existing methods had poor robustness; they not only had high error rate of target recognition but also had high dependence on parameters, so they were limited in application. Therefore, this paper proposed an image target detection and recognition method based on the improved R-CNN model, so as to detect and recognize the dynamic image target in real time. Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. In order to improve the accuracy and real-time performance of the model in image target detection and recognition, this paper adopted the target feature matching module in the existing R-CNN network model, so as to obtain the feature map close to the same target through similarity calculation for the features extracted by the model. Therefore, an image target detection and recognition algorithm based on the improved R-CNN network model is proposed. Finally, the experimental results showed that the image target detection and recognition algorithm proposed in this paper can be better applied to image target detection and classification in complex environment and had higher detection efficiency and recognition accuracy than the existing models. The target detection and recognition algorithm proposed in this paper had certain reference value and guiding significance for further application research in related fields.
Objective Nurse practitioners (NPs) have drawn significant attention recently and played a major role in healthcare. We aim to find the evaluation of NPs through published studies and then visualize the research status and hotspots in this field. Methods All data came from the Web of Science Core Collection, and the data were counted and entered into Excel 2016. The key documents nodes were excavated by analyzing the knowledge network map using CiteSpaceV software. In this study, these nodes of “author, country, institution, keyword, co-citation (reference\cited-author\cited-journal), and grant” were harvested for analysis and comparison. Results A total of 4912 records, which were published between 2007 and 2018 and pertained to NPs, were retrieved from the Web of Science Core Collection database (WoSCC) from a diversity of languages. The total and the annual number of publications and citations of these continually increased over time. Most publications were in 2018 (618 records). This study involved 8241 authors located in 98 countries and 4557 institutions totally. The United States (2737 records) and the University of Michigan (90 records) dominated in publication frequency. There are 902 journals and 2449 articles with funding support that have been analyzed. Most articles were from JAMA: The Journal of the American Medical Association (1386, IF = 47.661), followed by the Journal of Advanced Nursing (1359, IF = 2.267), and The New England Journal of Medicine (1109, IF = 79.258). The reference “The Role of Nurse Practitioners in Reinventing Primary Care” was co-cited most frequently, which revealed it as the highest landmark article in NP. The top-ranked keyword was “Care,” other than “Nurse practitioner,” which has an ultra-high frequency. Some of the high-frequency keywords represent the significant direction of NPs. Conclusions NPs are at the crux of health-care delivery and play an important role in providing high-quality nursing. Publications on NPs in WoSCC have increased notably during the recent years, and have appeared in some journals that have a high impact factor. Research frontiers and developmental trends were revealed by the analysis in this study, which can be used to forecast future research developments in NPs and taken as a reference to choose the right directions by subsequent researchers who wish to use these results. However, the grant support from administration or research institutions is still in need of improvement and the scope of research in NPs should be broadened in the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.