{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:34:28Z","timestamp":1783452868082,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cao Zeng","award":["2016YFE0200400"],"award-info":[{"award-number":["2016YFE0200400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter \u03b2 is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter \u03b2 provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).<\/jats:p>","DOI":"10.3390\/s21041240","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:12:10Z","timestamp":1613146330000},"page":"1240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes"],"prefix":"10.3390","volume":"21","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of High-Speed Circuit Design and EMC Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of High-Speed Circuit Design and EMC Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cao","family":"Zeng","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"ref_1","unstructured":"Suard, F., Rakotomamonjy, A., Bensrhair, A., and Broggi, A. (2006, January 13\u201315). Pedestrian Detection using Infrared images and Histograms of Oriented Gradients. Proceedings of the 2006 IEEE Intelligent Vehicles Symposium, Tokyo, Japan."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1109\/TVT.2004.834878","article-title":"Pedestrian detection for driver assistance using multiresolution infrared vision","volume":"53","author":"Bertozzi","year":"2004","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.cviu.2006.08.009","article-title":"Pedestrian detection and tracking in infrared imagery using shape and appearance","volume":"106","author":"Dai","year":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_4","unstructured":"Zhang, L., Wu, B., and Nevatia, R. (2007, January 16\u201320). Pedestrian detection in infrared images based on local shape features. In Proceeding of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, NJ, USA."},{"key":"ref_5","unstructured":"Fardi, B., Schuenert, U., and Wanielik, G. (2000, January 17\u201320). Shape and motion-based pedestrian detection in infrared images: A multi sensor approach. Proceeding of the IEEE Intelligent Vehicles Symposium, Toronto, ON, Canada."},{"key":"ref_6","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":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_9","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., and Darrell, T. (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_12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015, January 7\u201312). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Fu, C., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"Volume 39","author":"Ren","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_16","first-page":"970","article-title":"Nighttime Target Recognition Method Based on Infrared Thermal Imaging and YOLOv3","volume":"41","author":"Shi","year":"2019","journal-title":"Infrared Technol."},{"key":"ref_17","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Zhu, X. (2019, January 19\u201321). Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network. Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868430"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 16\u201320). Feature Selective Anchor-Free Module for Single-Shot Object Detection. Proceedings of the Computer Vision and Pattern Recognition, Piscataway, NJ, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_20","unstructured":"Lin, T., Dollar, P., and Girshick, R. (2005, January 20\u201325). Feature Pyramid Networks for Object Detection. Proceedings of the Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"Volume 34","author":"Zheng","year":"2020","journal-title":"Proceedings of the National Conference on Artificial Intelligence"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., and Chellappa, R. (2017, January 22\u201329). Soft-NMS\u2014Improving Object Detection with One Line of Code. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., and Girshick, R. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 15\u201319). Unitbox: An advanced object detection network. Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, UK.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_25","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep Sparse Rectifier Neural Networks. Proceedings of the International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/ICPR.2006.479","article-title":"Efficient non-maximum suppression","volume":"Volume 3","author":"Neubeck","year":"2006","journal-title":"Proceeding of the 18th International Conference on Pattern Recognition (ICPR\u201906)"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., and Belongie, S. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). Yolo9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_30","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). Dssd: Deconvolutional single shot detector. arXiv."},{"key":"ref_31","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_32","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2017). DOTA: A large-scale dataset for object detection in aerial images. arXiv.","DOI":"10.1109\/CVPR.2018.00418"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1240\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:22:21Z","timestamp":1760160141000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,10]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041240"],"URL":"https:\/\/doi.org\/10.3390\/s21041240","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,10]]}}}