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A multi-label location measurement method based on the NLM\u2013Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems.<\/jats:p>","DOI":"10.3390\/s25020426","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:42:15Z","timestamp":1736750535000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Novel RFID Multi-Label Network Location Measurement by Dual CCD and Non-Local Means\u2013Harris Algorithm"],"prefix":"10.3390","volume":"25","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Physical Education, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junji","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zelin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University Yangtze River Delta Institute of Optoelectronics, Nantong 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23332","DOI":"10.1109\/JIOT.2022.3202136","article-title":"A Portable RFID Localization Approach for Mobile Robots","volume":"9","author":"Xie","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1109\/TIE.2023.3250747","article-title":"Magnetic Permeability Perturbation-Based RFID Sensor for Pipeline Internal Corrosion Monitoring","volume":"71","author":"Wu","year":"2024","journal-title":"IEEE Trans. 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