{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T01:10:39Z","timestamp":1781053839928,"version":"3.54.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key R&D Program Projects in Zhejiang Province","award":["2024C01129"],"award-info":[{"award-number":["2024C01129"]}]},{"name":"Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10586-024-04595-0","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T17:03:21Z","timestamp":1719421401000},"page":"13379-13393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel real-time object detection method for complex road scenes based on YOLOv7-tiny"],"prefix":"10.1007","volume":"27","author":[{"given":"Yunfa","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"4595_CR1","doi-asserted-by":"crossref","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, p. (2001). Ieee","DOI":"10.1109\/CVPR.2001.990517"},{"key":"4595_CR2","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), vol. 1, pp. 886\u2013893 (2005). Ieee","DOI":"10.1109\/CVPR.2005.177"},{"key":"4595_CR3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.image.2018.09.002","volume":"70","author":"X Dai","year":"2019","unstructured":"Dai, X.: Hybridnet: a fast vehicle detection system for autonomous driving. Signal Proc. Image Commun. 70, 79\u201388 (2019)","journal-title":"Signal Proc. Image Commun."},{"key":"4595_CR4","doi-asserted-by":"crossref","unstructured":"Mao, J., Xiao, T., Jiang, Y., Cao, Z.: What can help pedestrian detection? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3127\u20133136 (2017)","DOI":"10.1109\/CVPR.2017.639"},{"issue":"11","key":"4595_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"4595_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"4595_CR7","doi-asserted-by":"publisher","DOI":"10.1145\/3065386","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Proc. Syst. (2012). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Adv. Neural Inform. Proc. Syst."},{"key":"4595_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"4595_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"4595_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"4595_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031","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 Inform. Proc. Syst. (2015). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"Adv. Neural Inform. Proc. Syst."},{"key":"4595_CR12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337 (2016). Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"4595_CR13","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":"4595_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"4595_CR15","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14, pp. 354\u2013370 (2016). Springer","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"4595_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"4595_CR17","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"1","key":"4595_CR18","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Royal Stat. Soc. 28(1), 100\u2013108 (1979)","journal-title":"J. Royal Stat. Soc."},{"key":"4595_CR19","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"4595_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"4595_CR21","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"4595_CR22","unstructured":"Jocher, G.: yolov5. Git code. Available online: https:\/\/github.com\/ultralytics\/yolov5 (2020)"},{"key":"4595_CR23","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al.: Yolov6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)"},{"key":"4595_CR24","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"4595_CR25","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Adv. Neural Inform. Proc. Syst. 28 (2015)"},{"key":"4595_CR26","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4595_CR27","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4595_CR28","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740\u2013755 (2014). Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"4595_CR29","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.patrec.2023.03.009","volume":"168","author":"B Mahaur","year":"2023","unstructured":"Mahaur, B., Mishra, K.: Small-object detection based on yolov5 in autonomous driving systems. Pattern Recognit. Lett. 168, 115\u2013122 (2023)","journal-title":"Pattern Recognit. Lett."},{"key":"4595_CR30","doi-asserted-by":"publisher","first-page":"103752","DOI":"10.1016\/j.jvcir.2023.103752","volume":"90","author":"M Wang","year":"2023","unstructured":"Wang, M., Yang, W., Wang, L., Chen, D., Wei, F., KeZiErBieKe, H., Liao, Y.: Fe-yolov5: feature enhancement network based on yolov5 for small object detection. J. Visual Commun. Image Represent. 90, 103752 (2023)","journal-title":"J. Visual Commun. Image Represent."},{"key":"4595_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02893-3","author":"W Sun","year":"2022","unstructured":"Sun, W., Dai, L., Zhang, X., Chang, P., He, X.: Rsod: real-time small object detection algorithm in uav-based traffic monitoring. Appl. Intell. (2022). https:\/\/doi.org\/10.1007\/s10489-021-02893-3","journal-title":"Appl. Intell."},{"key":"4595_CR32","first-page":"116675","volume":"104","author":"OC Koyun","year":"2022","unstructured":"Koyun, O.C., Keser, R.K., Akkaya, I.B., T\u00f6reyin, B.U.: Focus-and-detect: a small object detection framework for aerial images. Signal Proc.: Image Commun. 104, 116675 (2022)","journal-title":"Signal Proc.: Image Commun."},{"issue":"3","key":"4595_CR33","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1007\/s00521-021-06526-1","volume":"34","author":"J Chen","year":"2022","unstructured":"Chen, J., Jia, K., Chen, W., Lv, Z., Zhang, R.: A real-time and high-precision method for small traffic-signs recognition. Neural Comput. Appl. 34(3), 2233\u20132245 (2022)","journal-title":"Neural Comput. Appl."},{"issue":"10","key":"4595_CR34","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1049\/itr2.12212","volume":"16","author":"Q Su","year":"2022","unstructured":"Su, Q., Wang, H., Xie, M., Song, Y., Ma, S., Li, B., Yang, Y., Wang, L.: Real-time traffic cone detection for autonomous driving based on yolov4. IET Intell. Trans. Syst. 16(10), 1380\u20131390 (2022)","journal-title":"IET Intell. Trans. Syst."},{"key":"4595_CR35","first-page":"192","volume":"50","author":"A Grents","year":"2020","unstructured":"Grents, A., Varkentin, V., Goryaev, N.: Determining vehicle speed based on video using convolutional neural network. Trans. Res. Proc. 50, 192\u2013200 (2020)","journal-title":"Trans. Res. Proc."},{"key":"4595_CR36","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1007\/s00521-020-05331-6","volume":"33","author":"X Wang","year":"2021","unstructured":"Wang, X., Chen, X., Wang, Y.: Small vehicle classification in the wild using generative adversarial network. Neural Comput. Appl. 33, 5369\u20135379 (2021)","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"4595_CR37","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1007\/s11554-021-01121-y","volume":"18","author":"Y Yang","year":"2021","unstructured":"Yang, Y., Song, H., Sun, S., Zhang, W., Chen, Y., Rakal, L., Fang, Y.: A fast and effective video vehicle detection method leveraging feature fusion and proposal temporal link. J. Real-Time Image Proc. 18(4), 1261\u20131274 (2021)","journal-title":"J. Real-Time Image Proc."},{"key":"4595_CR38","doi-asserted-by":"publisher","first-page":"40701","DOI":"10.1109\/ACCESS.2022.3166923","volume":"10","author":"T Liang","year":"2022","unstructured":"Liang, T., Bao, H., Pan, W., Pan, F.: Alodad: an anchor-free lightweight object detector for autonomous driving. IEEE Access 10, 40701\u201340714 (2022)","journal-title":"IEEE Access"},{"issue":"11","key":"4595_CR39","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/34.730558","volume":"20","author":"L Itti","year":"1998","unstructured":"Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254\u20131259 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"4595_CR40","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341\u20132353 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"4595_CR41","first-page":"6","volume":"2","author":"F Yu","year":"2018","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T., et al.: Bdd100k: a diverse driving video database with scalable annotation tooling. Appl. Intell. 2(5), 6 (2018)","journal-title":"Appl. Intell."},{"key":"4595_CR42","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361 (2012). IEEE","DOI":"10.1109\/CVPR.2012.6248074"},{"issue":"4","key":"4595_CR43","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/e24040487","volume":"24","author":"Y Gu","year":"2022","unstructured":"Gu, Y., Si, B.: A novel lightweight real-time traffic sign detection integration framework based on yolov4. Entropy 24(4), 487 (2022)","journal-title":"Entropy"},{"key":"4595_CR44","doi-asserted-by":"publisher","first-page":"61546","DOI":"10.1109\/ACCESS.2023.3262601","volume":"11","author":"Z Li","year":"2023","unstructured":"Li, Z., Pang, C., Dong, C., Zeng, X.: R-yolov5: a lightweight rotational object detection algorithm for real-time detection of vehicles in dense scenes. IEEE Access 11, 61546\u201361559 (2023)","journal-title":"IEEE Access"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04595-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04595-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04595-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T22:00:59Z","timestamp":1727301659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04595-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":44,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["4595"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04595-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]},"assertion":[{"value":"10 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no confict of interests that are related to the submission of this publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}