{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:11:22Z","timestamp":1760127082737,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T00:00:00Z","timestamp":1683417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The minimum resolvable temperature difference (MRTD) at which a four-rod target can be resolved is a critical parameter used to assess the comprehensive performance of thermal imaging systems, which is important for technological innovation in military and other fields. Recently, there have been some attempts to use an automatic objective approach based on deep learning to take the place of the classical manual subjective MRTD measurement approach, which is strongly affected by the psychological subjective factors of the experimenter and is limited in accuracy and speed. However, the scale variability of four-rod targets and the low pixels of infrared thermal cameras have turned out to be a challenging problem for automatic MRTD measurement. We propose a multiscale deblurred feature extraction network (MDF-Net), a backbone based on a yolov5 neural network, in an attempt to solve the aforementioned problem. We first present a global attention mechanism (GAM) attention module to represent strong images of the four-rod targets. Next, a Rep VGG module is introduced to decrease the blur. Our experiments show that the proposed method achieves the desired effect and state-of-the-art detection results, which innovatively improve the accuracy of four-rod target detection to 82.3% and thus make it possible for the thermal imagers to see further and to respond faster and more accurately.<\/jats:p>","DOI":"10.3390\/s23094542","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:29:22Z","timestamp":1683512962000},"page":"4542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multiscale Deblurred Feature Extraction Network for Automatic Four-Rod Target Detection in MRTD Measuring Process of Thermal Imagers"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4807-3765","authenticated-orcid":false,"given":"Zhenggang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology of China, Dalian 116024, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5345-7190","authenticated-orcid":false,"given":"Wei","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology of China, Dalian 116024, China"}]},{"given":"Haibin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology of China, Dalian 116024, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"A25","DOI":"10.1051\/0004-6361\/201424973","article-title":"The visible and infrared survey telescope for astronomy (VISTA): Design, technical overview, and performance","volume":"575","author":"Sutherland","year":"2015","journal-title":"Astron. Astrophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/03091900512331333158","article-title":"A perspective on medical infrared imaging","volume":"29","author":"Jiang","year":"2005","journal-title":"J. Med. Eng. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/PROC.1975.9711","article-title":"The military applications of remote sensing by infrared","volume":"63","author":"Hudson","year":"1975","journal-title":"Proc. IEEE"},{"key":"ref_4","first-page":"1300","article-title":"Research and prospects of the domestic infrared thermography technology","volume":"44","author":"Mo","year":"2014","journal-title":"Laser Infrared"},{"key":"ref_5","unstructured":"Wang, Y., Gu, Z., Wang, S., and He, P. (2017, January 20\u201322). The temperature measurement technology of infrared thermal imaging and its applications review. Proceedings of the 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Yangzhou, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"R33","DOI":"10.1088\/0967-3334\/33\/3\/R33","article-title":"Infrared thermal imaging in medicine","volume":"33","author":"Ring","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.infrared.2012.03.007","article-title":"Medical applications of infrared thermography: A review","volume":"55","author":"Lahiri","year":"2012","journal-title":"Infrared Phys. Technol."},{"key":"ref_8","unstructured":"Cui, H., Xu, Y., Zeng, J., and Tang, Z. (2013, January 15\u201317). The methods in infrared thermal imaging diagnosis technology of power equipment. Proceedings of the 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication, Beijing, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3758\/BF03195520","article-title":"Infrared imaging technology and biological applications","volume":"35","author":"Kastberger","year":"2003","journal-title":"Behav. Res. Methods Instrum. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kolobrodov, V., and Mykytenko, V. (2015, January 14\u201318). Refinement of thermal imager minimum resolvable temperature difference calculating method. Proceedings of the Twelfth International Conference on Correlation Optics, Chernivtsi, Ukraine.","DOI":"10.1117\/12.2228532"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rvsc.2013.11.006","article-title":"Influence of environmental factors on infrared eye temperature measurements in cattle","volume":"96","author":"Church","year":"2014","journal-title":"Res. Vet. Sci."},{"key":"ref_12","first-page":"509","article-title":"Research on Minimum Resolvable Temoerature Difference Automatic Test Method of the Thermal Imaging System","volume":"32","author":"Chen","year":"2010","journal-title":"Infrared Technol."},{"key":"ref_13","unstructured":"Peri\u0107, D., and Livada, B. (2023, May 01). MRTD Measurements Role in Thermal Imager Quality Assessment. Available online: https:\/\/www.researchgate.net\/profile\/Branko-Livada\/publication\/334964614_MRTD_Measurements_Role_in_Thermal_Imager_Quality_Assessment\/links\/5d47d19e299bf1995b66419e\/MRTD-Measurements-Role-in-Thermal-Imager-Quality-Assessment.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"van Rheenen, A.D., Taule, P., Thomassen, J.B., and Madsen, E.B. (2018, January 17\u201318). MRTD: Man versus machine. Proceedings of the Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX, Orlando, FL, USA.","DOI":"10.1117\/12.2304581"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104111","DOI":"10.1117\/1.OE.58.10.104111","article-title":"Development and validation of a quantitative model for the subjective and objective minimum resolvable temperature difference of thermal imaging systems","volume":"58","author":"Khare","year":"2019","journal-title":"Opt. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/s12596-019-00584-4","article-title":"Objective evaluation method for advance thermal imagers based on minimum resolvable temperature difference","volume":"49","author":"Singh","year":"2020","journal-title":"J. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s12596-021-00730-x","article-title":"Auto-minimum resolvable temperature difference method for thermal imagers","volume":"50","author":"Singh","year":"2021","journal-title":"J. Opt."},{"key":"ref_18","first-page":"1","article-title":"Minimum resolvable temperature difference model, simulation, measurement and analysis","volume":"48","author":"Redjimi","year":"2016","journal-title":"Opt. Quantum Electron."},{"key":"ref_19","first-page":"671","article-title":"Virtual MRTD\u2014An indirect method to measure MRTD of thermal imagers using computer simulation","volume":"50","author":"Chrzanowski","year":"2020","journal-title":"Opt. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Irwin, A., and Grigor, J. (2014, January 6\u20138). An alternate method for performing MRTD measurements. Proceedings of the Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXV, Baltimore, MD, USA.","DOI":"10.1117\/12.2054401"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J.-h., and Jin, W.-q. (2009, January 17\u201319). Research and development on performance models of thermal imaging systems. Proceedings of the International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, Beijing, China.","DOI":"10.1117\/12.834693"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bijl, P., Hogervorst, M.A., and Valeton, J.M. (2002, January 3\u20135). TOD, NVTherm, and TRM3 model calculations: A comparison. Proceedings of the Infrared and Passive Millimeter-Wave Imaging Systems: Design, Analysis, Modeling, and Testing, Orlando, FL, USA.","DOI":"10.1117\/12.477475"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sun, J., and Ma, D. (2005, January 21\u201326). Intelligent MRTD testing for thermal imaging system using ANN. Proceedings of the ICO20: Remote Sensing and Infrared Devices and Systems, Changchun, China.","DOI":"10.1117\/12.668040"},{"key":"ref_24","unstructured":"Xu, L., Li, Q., and Lu, Y. (2018, January 11\u201313). Method of object MRTD-testing for thermal infrared imager. Proceedings of the Optical Design and Testing VIII, Beijing, China."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rong, W., Zhang, W., He, W., Chen, Q., Gu, G., Zhao, T., and Qiu, Z. (2019, January 26\u201328). A method of MRTD parameter measurement based on CNN neural network. Proceedings of the 2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, Beijing, China.","DOI":"10.1117\/12.2544098"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"11","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_27","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008, January 23\u201328). A discriminatively trained, multiscale, deformable part model. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Malisiewicz, T., Gupta, A., and Efros, A.A. (2011, January 6\u201313). Ensemble of exemplar-svms for object detection and beyond. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_33","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28, Available online: https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/14bfa6bb14875e45bba028a21ed38046-Paper.pdf."},{"key":"ref_34","unstructured":"Szegedy, C., Toshev, A., and Erhan, D. (2013). Deep neural networks for object detection. Adv. Neural Inf. Process. Syst., 26, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2013\/file\/f7cade80b7cc92b991cf4d2806d6bd78-Paper.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016;, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 27\u201328). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_38","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"063104","DOI":"10.1117\/1.OE.61.6.063104","article-title":"Concealed multiscale feature extraction network for automatic four-bar target detection in infrared imaging","volume":"61","author":"Tao","year":"2022","journal-title":"Opt. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:47Z","timestamp":1760124647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,7]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094542"],"URL":"https:\/\/doi.org\/10.3390\/s23094542","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,5,7]]}}}