{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:10:55Z","timestamp":1763705455794,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,12]],"date-time":"2018-05-12T00:00:00Z","timestamp":1526083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61640209"],"award-info":[{"award-number":["61640209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation for Distinguished Young Talents of Guizhou Province","award":["QKHRZ[2015]13"],"award-info":[{"award-number":["QKHRZ[2015]13"]}]},{"DOI":"10.13039\/501100004001","name":"Science and Technology Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["JZ[2014]2004, JZ[2014]2001, ZDZX[2013]6020, and LH[2016]7433"],"award-info":[{"award-number":["JZ[2014]2004, JZ[2014]2001, ZDZX[2013]6020, and LH[2016]7433"]}],"id":[{"id":"10.13039\/501100004001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Brazilian National Council for Scientific and Technological Development","award":["203076\/2015-0"],"award-info":[{"award-number":["203076\/2015-0"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CISE\/IIS 1231671, CISE\/IIS\/1427345"],"award-info":[{"award-number":["CISE\/IIS 1231671, CISE\/IIS\/1427345"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy.<\/jats:p>","DOI":"10.3390\/s18051530","type":"journal-article","created":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T02:57:20Z","timestamp":1526266640000},"page":"1530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-5195","authenticated-orcid":false,"given":"Guanci","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1915-9487","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74074, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Junior","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74074, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-6000","authenticated-orcid":false,"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vines, J., Lindsay, S., Pritchard, G.W., Lie, M., Greathead, D., Olivier, P., and Brittain, K. 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