{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T21:49:54Z","timestamp":1766267394369,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,7]],"date-time":"2018-06-07T00:00:00Z","timestamp":1528329600000},"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":["61772125, 61402097"],"award-info":[{"award-number":["61772125, 61402097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Doctoral Research Foundation of China","award":["20170520238"],"award-info":[{"award-number":["20170520238"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.<\/jats:p>","DOI":"10.3390\/s18061871","type":"journal-article","created":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T03:13:18Z","timestamp":1528427598000},"page":"1871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1243-0123","authenticated-orcid":false,"given":"Keming","family":"Mao\u00a0","sequence":"first","affiliation":[{"name":"College of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duo","family":"Lu\u00a0","sequence":"additional","affiliation":[{"name":"College of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dazhi","family":"E\u00a0","sequence":"additional","affiliation":[{"name":"Shenyang Fire Research Institute, Ministry of Public Security, Shenyang 110034, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9870-8925","authenticated-orcid":false,"given":"Zhenhua","family":"Tan\u00a0","sequence":"additional","affiliation":[{"name":"College of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,7]]},"reference":[{"key":"ref_1","unstructured":"(2011). Inspection Methods for Trace and Physical Evidences from Fire Scene\u2014Part 3: Ferrous Metal Work, National Standard of People\u2019s Republic of China. GB\/T 27905.3-2011."},{"key":"ref_2","unstructured":"Wu, Y., Zhao, C., Di, M., and Qi, Z. (2007, January 11\u201313). Application of metal oxidation theory in fire trace evidence identification. Proceedings of the Building Electrical and Intelligent System, Shenyang, China."},{"key":"ref_3","unstructured":"Wu, Y., Zhao, C., Di, M., and Qi, Z. (2008, January 27\u201328). Application of metal oxidation theory in fire investigation and fire safety. Proceedings of the International Colloquium on Safety Science and Technology, Shenyang, China."},{"key":"ref_4","first-page":"853","article-title":"Fuzzy identification of surface temperature for building members after fire","volume":"45","author":"Xu","year":"2005","journal-title":"J. Dalian Univ. Technol."},{"key":"ref_5","first-page":"176","article-title":"Analysis of surface discoloration of galvanizing sheet steel caused by unfavorable brazing heating","volume":"43","author":"Li","year":"2007","journal-title":"J. Phys. Test. Chem. Anal. Part A Phys. Test."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object Recognition from Local Scale-Invariant Features. Proceedings of the IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","unstructured":"Navneet, D., and Bill, T. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and van Gool, L. (2006, January 7\u201313). SURF: Speeded Up Robust Features. Proceedings of the 9th European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1364\/JOSAA.2.001160","article-title":"Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters","volume":"2","author":"John","year":"1985","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/TGRS.2016.2623742","article-title":"Discriminative Low-Rank Gabor Filtering for Spectral-Spatial Hyperspectral Image Classification","volume":"55","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","unstructured":"Wang, X., Han, T.X., and Yan, S. (October, January 27). An HOG-LBP human detector with partial occlusion handling. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_13","unstructured":"Fei-Fei, L., Fergus, R., and Torralba, A. (2018, March 18). Recognizing and Learning Object Categories. CVPR 2007 Short Course. Available online: http:\/\/people.csail.mit.edu\/torralba\/shortCourseRLOC\/."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Grauman, K., and Darrell, T. (2005, January 17\u201321). The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. Proceedings of the 10th IEEE International Conference on Computer Vision, Beijing, China.","DOI":"10.1109\/ICCV.2005.239"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C., and P\u00e9rez, P. (2010, January 13\u201318). Aggregating local descriptors into a compact image representation. Proceedings of the 23th IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Perronnin, F., and Dance, C. (2007, January 18\u201323). Fisher Kernels on Visual Vocabularies for Image Categorization. Proceedings of the 20th IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383266"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the 13th European Conference on Computer Vision (ECCV 5), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient Based Learning Applied to Document Recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_23","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NE, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","unstructured":"Faghih-Roohi, S., Hajizadeh, S., N\u00fa\u00f1ez, A., Babuska, R., and De Schutter, B. (22016, January 24\u201329). Deep convolutional neural networks for detection of rail surface defects. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, G., Tang, X., Lu, J., and Hu, J. (2017). An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17081729"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Psuj, G. (2018). Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements. Sensors, 18.","DOI":"10.3390\/s18010292"},{"key":"ref_31","first-page":"123","article-title":"Classification of surface defects on steel sheet using convolutional neural networks","volume":"51","author":"Zhou","year":"2017","journal-title":"Mater. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks","volume":"32","author":"Cha","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., and B\u00fcy\u00fck\u00f6zt\u00fcrk, O. (2017). Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types. Comput.-Aided Civ. Infrastruct. Eng.","DOI":"10.1111\/mice.12334"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, Y., Bao, Y., Chen, J., Zuo, W., and Li, H. (2018). Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Struct. Health Monit.","DOI":"10.1177\/1475921718764873"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the 31th AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_39","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (arXiv, 2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv."},{"key":"ref_40","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"Tran, T., Pham, T., Carneiro, G., Palmer, L., and Reid, I. (2017, January 4\u20139). A Bayesian Data Augmentation Approach for Learning Deep Models. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ding, J., Li, X., and Gudivada, V.N. (2017, January 11\u201314). Augmentation and evaluation of training data for deep learning. Proceedings of the IEEE International Conference on Big Data, Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258220"},{"key":"ref_43","unstructured":"Simon, M., Rodner, E., and Denzler, J. (arXiv, 2016). ImageNet pre-trained models with batch normalization, arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2017.05.027","article-title":"Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods","volume":"140","author":"Senthilnath","year":"2017","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:07:47Z","timestamp":1760195267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/6\/1871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,7]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["s18061871"],"URL":"https:\/\/doi.org\/10.3390\/s18061871","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,6,7]]}}}