{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:51:40Z","timestamp":1769997100081,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"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>Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection and tracking, comprising three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. We designed a CNN-based real-time detection model with a high recall rate for the first component to detect potential object regions in the entire image. The KCF algorithm and the designed lightweight CNN-based target detection model, which parallelly lock on the target more precisely in the target potential area, were used in the second component. In the final component, we designed an optimized Kalman filter to estimate the target\u2019s trajectory. We validated our method on a public dataset. The results show that the proposed real-time detection and tracking framework for infrared vehicle small targets could steadily track vehicle targets and adapt well in situations such as the temporary disappearance of targets and interference from other vehicles.<\/jats:p>","DOI":"10.3390\/s23094240","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T01:37:01Z","timestamp":1682386621000},"page":"4240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9187-2211","authenticated-orcid":false,"given":"Jun","family":"Fan","sequence":"first","affiliation":[{"name":"Army Aviation Institute, Beijing 101121, China"}]},{"given":"Jingbiao","family":"Wei","sequence":"additional","affiliation":[{"name":"Army Aviation Institute, Beijing 101121, China"}]},{"given":"Hai","family":"Huang","sequence":"additional","affiliation":[{"name":"Army Aviation Institute, Beijing 101121, China"}]},{"given":"Dafeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Army Aviation Institute, Beijing 101121, China"}]},{"given":"Ce","family":"Chen","sequence":"additional","affiliation":[{"name":"Army Aviation Institute, Beijing 101121, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2012, January 7\u201313). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Florence, Italy."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_4","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_5","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_6","first-page":"2","article-title":"ultralytics\/yolov5: v6.1-TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference","volume":"2","author":"Jocher","year":"2022","journal-title":"Zenodo"},{"key":"ref_7","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_8","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_9","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_10","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_11","unstructured":"Huang, L., Yang, Y., Deng, Y., and Yu, Y. (2015). Densebox: Unifying landmark localization with end to end object detection. arXiv."},{"key":"ref_12","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_13","unstructured":"Law, H., Teng, Y., Russakovsky, O., and Deng, J. (2019). Cornernet-lite: Efficient keypoint based object detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Krahenbuhl, P. (2019, January 15\u201320). Bottom-Up Object Detection by Grouping Extreme and Center Points. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, J., Chen, J., Cho, Y.K., Kang, D.Y., and Son, B.J. (2019). CNN-based person detection using infrared images for night-time intrusion warning systems. Sensors, 20.","DOI":"10.3390\/s20010034"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yao, S., Zhu, Q., Zhang, T., Cui, W., and Yan, P. (2022). Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features. Electronics, 11.","DOI":"10.3390\/electronics11060933"},{"key":"ref_17","first-page":"1","article-title":"A spatial-temporal feature-based detection framework for infrared dim small target","volume":"60","author":"Du","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","first-page":"1","article-title":"Cross-connected bidirectional pyramid network for infrared small-dim target detection","volume":"19","author":"Bai","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102949","DOI":"10.1016\/j.dsp.2020.102949","article-title":"Detection and tracking of infrared small target by jointly using SSD and pipeline filter","volume":"110","author":"Ding","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103733","DOI":"10.1016\/j.dsp.2022.103733","article-title":"Infrared dim and small targets detection via self-attention mechanism and pipeline correlator","volume":"130","author":"Lan","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3141584","article-title":"ISTDU-Net: Infrared Small-Target Detection U-Net","volume":"19","author":"Hou","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1007\/s11760-021-01936-z","article-title":"Infrared small target detection based on region proposal and CNN classifier","volume":"15","author":"Fan","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103659","DOI":"10.1016\/j.infrared.2021.103659","article-title":"ISTDet: An efficient end-to-end neural network for infrared small target detection","volume":"114","author":"Ju","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3141584","article-title":"RISTDnet: Robust infrared small target detection network","volume":"19","author":"Hou","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang, Y. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xi, X., Wang, J., Li, F., and Li, D. (2022). IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps. Electronics, 11.","DOI":"10.3390\/electronics11142154"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103615","DOI":"10.1016\/j.engappai.2020.103615","article-title":"STDnet: Exploiting high resolution feature maps for small object detection","volume":"91","author":"Bosquet","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107929","DOI":"10.1016\/j.patcog.2021.107929","article-title":"STDnet-ST: Spatio-temporal ConvNet for small object detection","volume":"116","author":"Bosquet","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-Speed Tracking with Kernelized Correlation Filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","first-page":"206","article-title":"A dataset for infrared time-sensitive target detection and tracking for air-ground application","volume":"7","author":"Fu","year":"2022","journal-title":"China Sci. Data"},{"key":"ref_31","first-page":"747","article-title":"Fast small target tracking in IR imagery based on improved similarity measure","volume":"Volume 9301","author":"Hou","year":"2014","journal-title":"Proceedings of the International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s11042-012-1258-y","article-title":"Detecting and tracking dim small targets in infrared image sequences under complex backgrounds","volume":"71","author":"Li","year":"2014","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","first-page":"510","article-title":"Infrared dim and small target tracking method incorporating statistical characteristics","volume":"Volume 10157","author":"Zhang","year":"2016","journal-title":"Infrared Technology and Applications, and Robot Sensing and Advanced Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.infrared.2017.02.002","article-title":"Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter","volume":"82","author":"Qian","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_35","unstructured":"Yun, S., and Kim, S. (2019). Pattern Recognition and Tracking, SPIE."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103102","DOI":"10.1016\/j.infrared.2019.103102","article-title":"Tracking small targets in infrared image sequences under complex environmental conditions","volume":"104","author":"Xiao","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2019.103116","article-title":"Target tracking from infrared imagery via an improved appearance model","volume":"104","author":"Zhao","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, Z., Chen, Z., Xie, Y., and Li, Z. (2021, January 5\u20137). Infrared Dim Small Target Tracking Based on Inter-frame Consistency Under Complex Background. Proceedings of the International Conference on Artificial Intelligence and Computer Engineering, Hangzhou, China.","DOI":"10.1109\/ICAICE54393.2021.00142"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huo, Y., Chen, Y., Zhang, H., Zhang, H., and Wang, H. (2022). Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion. Electronics, 11.","DOI":"10.3390\/electronics11152457"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fan, Y., Qiu, Q., Hou, S., Li, Y., Xie, J., Qin, M., and Chu, F. (2022). Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection. Electronics, 11.","DOI":"10.3390\/electronics11152344"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1049\/ipr2.12331","article-title":"Road infrared target detection with I-YOLO","volume":"16","author":"Sun","year":"2022","journal-title":"IET Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhou, X., Jiang, L., Hu, C., Lei, S., Zhang, T., and Mou, X. (2022). YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds. Sensors, 22.","DOI":"10.3390\/s22124600"},{"key":"ref_43","first-page":"126170J","article-title":"ECA-YOLOv5: Multi scale infrared salient target detection algorithm based on anchor free network","volume":"Volume 12617","author":"Li","year":"2023","journal-title":"Ninth Symposium on Novel Photoelectronic Detection Technology and Applications"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"116402","DOI":"10.1016\/j.image.2021.116402","article-title":"FD-SSD: An improved SSD object detection algorithm based on feature fusion and dilated convolution","volume":"98","author":"Yin","year":"2021","journal-title":"Signal Process. Image Commun."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"24344","DOI":"10.1109\/ACCESS.2020.2971026","article-title":"DF-SSD: An improved SSD object detection algorithm based on DenseNet and feature fusion","volume":"8","author":"Zhai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lim, J.S., Astrid, M., Yoon, H.J., and Lee, S.I. (2021, January 13\u201316). Small object detection using context and attention. Proceedings of the International Conference on Artificial Intelligence in Information and Communication, Jeju Island, Republic of Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415217"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4240\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:22:30Z","timestamp":1760124150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094240"],"URL":"https:\/\/doi.org\/10.3390\/s23094240","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,24]]}}}