{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T19:41:01Z","timestamp":1773862861178,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s11554-021-01170-3","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T10:29:38Z","timestamp":1632220178000},"page":"133-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects"],"prefix":"10.1007","volume":"19","author":[{"given":"A. N.","family":"Amudhan","sequence":"first","affiliation":[]},{"given":"Shah Rutvik","family":"Vrajesh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0644-3702","authenticated-orcid":false,"given":"A. P.","family":"Sudheer","sequence":"additional","affiliation":[]},{"given":"A.","family":"Lijiya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"1170_CR1","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1080\/01431160600746456","volume":"28","author":"D Lu","year":"2007","unstructured":"Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823\u2013870 (2007). https:\/\/doi.org\/10.1080\/01431160600746456","journal-title":"Int. J. Remote Sens."},{"key":"1170_CR2","first-page":"1","volume":"59","author":"D Hong","year":"2020","unstructured":"Hong, D., Gao, L., Yao, J., Zhang, B.: Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59, 1\u201313 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1170_CR3","doi-asserted-by":"publisher","first-page":"2970","DOI":"10.21090\/IJAERD.030144","volume":"3","author":"SH Parekh","year":"2016","unstructured":"Parekh, S.H., Thakore, G.D., Jaliya, U.K.: A survey on object detection and tracking. Int. J. Adv. Eng. Res. Dev. 3, 2970\u20132978 (2016). https:\/\/doi.org\/10.21090\/IJAERD.030144","journal-title":"Int. J. Adv. Eng. Res. Dev."},{"key":"1170_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","volume":"70","author":"A Garcia-Garcia","year":"2018","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., Garcia-Rodriguez, J.: A survey on deep learning techniques for image and video semantic segmentation. Appl. Soft Comput. 70, 41\u201365 (2018). https:\/\/doi.org\/10.1016\/j.asoc.2018.05.018","journal-title":"Appl. Soft Comput."},{"key":"1170_CR5","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv Prepr. arXiv1704.06857. (2017)","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"1170_CR6","doi-asserted-by":"crossref","unstructured":"De Brabandere, B., Neven, D., Van Gool, L.: Semantic Instance Segmentation with a Discriminative Loss Function. {arXiv Prepr. arXiv1708.02551. (2017)","DOI":"10.1109\/CVPRW.2017.66"},{"key":"1170_CR7","doi-asserted-by":"crossref","unstructured":"Romera-Paredes, B., Hilaire, P., Torr, S.: Recurrent Instance Segmentation. In: European conference on computer vision. pp. 312\u2013329. Springer (2016)","DOI":"10.1007\/978-3-319-46466-4_19"},{"key":"1170_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/J.NEUCOM.2019.11.023","volume":"381","author":"G Ciaparrone","year":"2020","unstructured":"Ciaparrone, G., Luque S\u00e1nchez, F., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: a survey. Neurocomputing 381, 61\u201388 (2020). https:\/\/doi.org\/10.1016\/J.NEUCOM.2019.11.023","journal-title":"Neurocomputing"},{"key":"1170_CR9","doi-asserted-by":"publisher","first-page":"2666","DOI":"10.1109\/TAES.2013.6621844","volume":"49","author":"S Reuter","year":"2013","unstructured":"Reuter, S., Wilking, B., Wiest, J., Munz, M.: Real-time multi-object tracking using random finite sets. IEEE Trans. Aerosp. Electron. Syst. 49, 2666\u20132678 (2013)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"1170_CR10","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1007\/s11554-020-01025-3","volume":"18","author":"L Yang","year":"2021","unstructured":"Yang, L., Qin, Y., Zhang, X.: Lightweight densely connected residual network for human pose estimation. J. Real-Time Image Process. 18, 825\u2013837 (2021). https:\/\/doi.org\/10.1007\/s11554-020-01025-3","journal-title":"J. Real-Time Image Process."},{"key":"1170_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.102897","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Tian, Y., He, M.: Monocular human pose estimation: a survey of deep learning-based methods. Comput. Vis. Image Underst. (2020). https:\/\/doi.org\/10.1016\/j.cviu.2019.102897","journal-title":"Comput. Vis. Image Underst."},{"key":"1170_CR12","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1088\/1361-6560\/ab843e","volume":"65","author":"Y Fu","year":"2020","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.: Deep learning in medical image registration: a review. Phys. Med. Biol. 65, 20\u201321 (2020). https:\/\/doi.org\/10.1088\/1361-6560\/ab843e","journal-title":"Phys. Med. Biol."},{"key":"1170_CR13","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/S11554-018-0847-Z","volume":"17","author":"R Nandalike","year":"2019","unstructured":"Nandalike, R., Sarojadevi, H.: Multimodal image feature detection with ROI-based optimization for image registration. J. Real-Time Image Process. 17, 1007\u20131013 (2019). https:\/\/doi.org\/10.1007\/S11554-018-0847-Z","journal-title":"J. Real-Time Image Process."},{"key":"1170_CR14","doi-asserted-by":"crossref","unstructured":"Farfade, S.S., Saberian, M.J., Li, L.-J.: Multi-view Face Detection Using Deep Convolutional Neural Networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. pp. 643\u2013650. ACM, New York, NY, USA (2015)","DOI":"10.1145\/2671188.2749408"},{"key":"1170_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"J-G Lee","year":"2017","unstructured":"Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570 (2017)","journal-title":"Korean J. Radiol."},{"key":"1170_CR16","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905). pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"1170_CR17","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","volume":"28","author":"T Ahonen","year":"2006","unstructured":"Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037\u20132041 (2006). https:\/\/doi.org\/10.1109\/TPAMI.2006.244","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1170_CR18","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346\u2013359 (2008). https:\/\/doi.org\/10.1016\/j.cviu.2007.09.014","journal-title":"Comput. Vis. Image Underst."},{"key":"1170_CR19","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 404\u2013417 (2006)","DOI":"10.1007\/11744023_32"},{"key":"1170_CR20","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li-Jia, Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1170_CR21","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1170_CR22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J., Berkeley, U.C., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1170_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: IEEE international conference on computer vision. pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1170_CR24","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1170_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2","author":"W Liu","year":"2015","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. Eur. Conf. Comput. Vis. (2015). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","journal-title":"Eur. Conf. Comput. Vis."},{"key":"1170_CR26","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. In: IEEE conference on computer vision and pattern recognition. pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1170_CR27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263\u20137271. Institute of Electrical and Electronics Engineers Inc. (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1170_CR28","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv Prepr. arXiv. (2018)"},{"key":"1170_CR29","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv Prepr. arXiv2004.10934. (2020)"},{"key":"1170_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal Loss for Dense Object Detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1170_CR31","unstructured":"Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: DSSD: Deconvolutional single shot detector. arXiv Prepr. arXiv1701.06659. (2017)"},{"key":"1170_CR32","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path Aggregation Network for Instance Segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1170_CR33","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1170_CR34","unstructured":"Tan, M., Le, Q. V.: EfficientNet: Rethinking model scaling for convolutional neural networks. In: 36th International Conference on Machine Learning, ICML 2019. pp. 10691\u201310700. International Machine Learning Society (IMLS) (2019)"},{"key":"1170_CR35","doi-asserted-by":"crossref","unstructured":"Lee, Y., Park, J.: CenterMask\u202f: Real-Time Anchor-Free Instance Segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 13906\u201313915 (2020)","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"1170_CR36","doi-asserted-by":"publisher","unstructured":"Jocher, G., Stoken, A., Borovec, J., NanoCode012, Chaurasia, A., And, T., And, L.C., And, A. V, And, L., And, T., And, Y., And, A.H., And, L., And, A., And, J.H., And, L.D., And, M., And, Y.K., And, O., And, W., And, Y.D., And, A.L., And, M., And, B.M., And, B.F., And, D.K., And, D.Y., And, D., And, D., Ingham}, F.: ultralytics\/yolov5: v5.0-YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations (2021). https:\/\/doi.org\/10.5281\/zenodo.4679653","DOI":"10.5281\/zenodo.4679653"},{"key":"1170_CR37","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.imavis.2019.04.007","volume":"87","author":"Hendry","year":"2019","unstructured":"Hendry, Chen, R.-C.: Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47\u201356 (2019). https:\/\/doi.org\/10.1016\/j.imavis.2019.04.007","journal-title":"Image Vis. Comput."},{"key":"1170_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2019.163767","author":"X Sun","year":"2020","unstructured":"Sun, X., Gu, J., Huang, R.: A modified SSD method for electronic components fast recognition. Optik (Stuttg) (2020). https:\/\/doi.org\/10.1016\/j.ijleo.2019.163767","journal-title":"Optik (Stuttg)"},{"key":"1170_CR39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1170_CR40","doi-asserted-by":"publisher","first-page":"106838","DOI":"10.1109\/ACCESS.2019.2932731","volume":"7","author":"C Cao","year":"2019","unstructured":"Cao, C., Wang, B., Zhang, W., Zeng, X., Yan, X.: An improved faster R-CNN for small object detection. IEEE Access. 7, 106838\u2013106846 (2019)","journal-title":"IEEE Access."},{"key":"1170_CR41","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: IEEE international conference on computer vision. pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1170_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105590","volume":"194","author":"F P\u00e9rez-Hern\u00e1ndez","year":"2020","unstructured":"P\u00e9rez-Hern\u00e1ndez, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., Herrera, F.: Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: application in video surveillance. Knowledge-Based Syst. 194, 105590 (2020). https:\/\/doi.org\/10.1016\/j.knosys.2020.105590","journal-title":"Knowledge-Based Syst."},{"key":"1170_CR43","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.imavis.2019.04.007","volume":"87","author":"R-CC Hendry","year":"2019","unstructured":"Hendry, R.-C.C.: Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47\u201356 (2019). https:\/\/doi.org\/10.1016\/j.imavis.2019.04.007","journal-title":"Image Vis. Comput."},{"key":"1170_CR44","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TVT.2012.2226218","volume":"62","author":"G-S Hsu","year":"2013","unstructured":"Hsu, G.-S., Chen, J.-C., Chung, Y.-Z.: Application-oriented license plate recognition. IEEE Trans. Veh. Technol. 62, 552\u2013561 (2013). https:\/\/doi.org\/10.1109\/TVT.2012.2226218","journal-title":"IEEE Trans. Veh. Technol."},{"key":"1170_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103615","volume":"91","author":"B Bosquet","year":"2020","unstructured":"Bosquet, B., Mucientes, M., Brea, V.M.: STDnet: exploiting high resolution feature maps for small object detection. Eng. Appl. Artif. Intell. 91, 103615 (2020). https:\/\/doi.org\/10.1016\/j.engappai.2020.103615","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1170_CR46","unstructured":"Cui, L., Ma, R., Lv, P., Jiang, X., Gao, Z., Zhou, B., Xu, M.: MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects. arXiv. 2\u20134 (2018)"},{"key":"1170_CR47","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1016\/j.cja.2020.02.024","volume":"33","author":"Y Li","year":"2020","unstructured":"Li, Y., Dong, H., Li, H., Zhang, X., Zhang, B., Xiao, Z.: Multi-block SSD based on small object detection for UAV railway scene surveillance. Chin. J. Aeronaut. 33, 1747\u20131755 (2020). https:\/\/doi.org\/10.1016\/j.cja.2020.02.024","journal-title":"Chin. J. Aeronaut."},{"key":"1170_CR48","doi-asserted-by":"crossref","unstructured":"Luo, H.-W., Zhang, C.-S., Pan, F.-C., Ju, X.-M.: Contextual-YOLOV3: Implement Better Small Object Detection Based Deep Learning. In: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). pp. 134\u2013141. IEEE (2019)","DOI":"10.1109\/MLBDBI48998.2019.00032"},{"key":"1170_CR49","doi-asserted-by":"crossref","unstructured":"Hu, P., Ramanan, D.: Finding Tiny Faces Supplementary Materials. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 951\u2013959 (2017)","DOI":"10.1109\/CVPR.2017.166"},{"key":"1170_CR50","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, M.-Y., Tuzel, O., Xiao, J.: R-CNN for Small Object Detection. In: n Asian conference on computer vision. pp. 214\u2013230. Springe, Cham (2017)","DOI":"10.1007\/978-3-319-54193-8_14"},{"key":"1170_CR51","doi-asserted-by":"crossref","unstructured":"Du, P., Qu, X., Wei, T., Peng, C., Zhong, X., Chen, C.: Research on Small-size Object Detection in Complex Background. In: 2018 Chinese Automation Congress (CAC). pp. 4216\u20134220. IEEE (2018)","DOI":"10.1109\/CAC.2018.8623078"},{"key":"1170_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1\u20139. IEEE Computer Society (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1170_CR53","doi-asserted-by":"crossref","unstructured":"Huang, R., Pedoeem, J., Chen, C.: YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. In: 2018 IEEE International Conference on Big Data (Big Data). pp. 2503\u20132510. IEEE (2018)","DOI":"10.1109\/BigData.2018.8621865"},{"key":"1170_CR54","doi-asserted-by":"publisher","first-page":"133529","DOI":"10.1109\/ACCESS.2019.2941547","volume":"7","author":"Q-C Mao","year":"2019","unstructured":"Mao, Q.-C., Sun, H.-M., Liu, Y.-B., Jia, R.-S.: Mini-YOLOv3: real-time object detector for embedded applications. IEEE Access. 7, 133529\u2013133538 (2019)","journal-title":"IEEE Access."},{"key":"1170_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2020.102756","volume":"102","author":"Y Yin","year":"2020","unstructured":"Yin, Y., Li, H., Fu, W.: Faster-YOLO: An accurate and faster object detection method. Digit. Signal Process. 102, 102756 (2020). https:\/\/doi.org\/10.1016\/j.dsp.2020.102756","journal-title":"Digit. Signal Process."},{"key":"1170_CR56","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Iandola, F., Jin, P.H., Keutzer, K.: SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving. arXiv Prepr. 129\u2013137 (2016)","DOI":"10.1109\/CVPRW.2017.60"},{"key":"1170_CR57","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1109\/ACCESS.2019.2961959","volume":"8","author":"W Fang","year":"2020","unstructured":"Fang, W., Wang, L., Ren, P.: Tinier-YOLO: a real-time object detection method for constrained environments. IEEE Access. 8, 1935\u20131944 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2019.2961959","journal-title":"IEEE Access."},{"key":"1170_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3189691","volume":"2020","author":"N Nguyen","year":"2020","unstructured":"Nguyen, N., Do, T., Ngo, T.D., Le, D.: An evaluation of deep learning methods for small object detection. J. Electr. Comput. Eng. 2020, 1 (2020)","journal-title":"J. Electr. Comput. Eng."},{"key":"1170_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114602","volume":"172","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Sun, P., Wergeles, N., Shang, Y.: A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Appl. 172, 114602 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.114602","journal-title":"Expert Syst. Appl."},{"key":"1170_CR60","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","volume":"522","author":"Z Huang","year":"2020","unstructured":"Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., Wang, R.: DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Inf. Sci. (Ny) 522, 241\u2013258 (2020). https:\/\/doi.org\/10.1016\/j.ins.2020.02.067","journal-title":"Inf. Sci. (Ny)"},{"key":"1170_CR61","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., Zuo, W.: Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation. arXiv Prepr. arXiv2005.03572. (2020)","DOI":"10.1109\/TCYB.2021.3095305"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-021-01170-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-021-01170-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-021-01170-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T17:11:26Z","timestamp":1725815486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-021-01170-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,21]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["1170"],"URL":"https:\/\/doi.org\/10.1007\/s11554-021-01170-3","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,21]]},"assertion":[{"value":"26 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}