{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:20:10Z","timestamp":1772191210575,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Foundation of China","doi-asserted-by":"crossref","award":["51975490"],"award-info":[{"award-number":["51975490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Projects of Sichuan","award":["23QYCX0280"],"award-info":[{"award-number":["23QYCX0280"]}]},{"name":"Science and Technology Projects of Sichuan","award":["2022JDRC0075"],"award-info":[{"award-number":["2022JDRC0075"]}]},{"name":"Science and Technology Projects of Sichuan","award":["2022NSFSC0461"],"award-info":[{"award-number":["2022NSFSC0461"]}]},{"name":"Science and Technology Projects of Yibin","award":["SWJTU2021020001"],"award-info":[{"award-number":["SWJTU2021020001"]}]},{"name":"Science and Technology Projects of Yibin","award":["SWJTU2021020002"],"award-info":[{"award-number":["SWJTU2021020002"]}]},{"name":"Science and Technology Projects of Chengdu","award":["2021YF0800138GX"],"award-info":[{"award-number":["2021YF0800138GX"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10689-y","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T07:51:26Z","timestamp":1733212286000},"page":"2327-2355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vulnerable road users\u2019 detection with bionic-corrected multi-fisheye images and safety warning for ART"],"prefix":"10.1007","volume":"37","author":[{"given":"Jirui","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yongzhao","family":"Han","sequence":"additional","affiliation":[]},{"given":"Hongjie","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Fujian","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiaoyi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiangfan","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2641-2049","authenticated-orcid":false,"given":"Zutao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"10689_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.125472","volume":"262","author":"J Chen","year":"2023","unstructured":"Chen J, Fang Z, Azam A, Wu X, Zhang Z, Lu L, Li D (2023) An energy self-circulation system based on the wearable thermoelectric harvester for ART driver monitoring. Energy 262:125472. https:\/\/doi.org\/10.1016\/j.energy.2022.125472","journal-title":"Energy"},{"key":"10689_CR2","doi-asserted-by":"publisher","unstructured":"Yuan, X., Zhang, Q., Zhang, S., Huang, R., Zhang, X., & Yunqin, H. (2020, November). Longitudinal control of autonomous-rail rapid tram in platooning using model predictive control. In 2020 IEEE Vehicle Power and Propulsion Conference (VPPC) (pp. 1\u20135). IEEE. https:\/\/doi.org\/10.1109\/VPPC49601.2020.9330878.","DOI":"10.1109\/VPPC49601.2020.9330878"},{"issue":"2","key":"10689_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5038\/2375-0901.5.2.1","volume":"5","author":"HS Levinson","year":"2002","unstructured":"Levinson HS, Zimmerman S, Clinger J, Rutherford HCS (2002) Bus rapid transit: An overview. J Public Transp 5(2):1\u201330. https:\/\/doi.org\/10.5038\/2375-0901.5.2.1","journal-title":"J Public Transp"},{"key":"10689_CR4","doi-asserted-by":"publisher","first-page":"46","DOI":"10.19713\/j.cnki.43-1423\/u.t20200245","volume":"01","author":"L Xiaocong","year":"2021","unstructured":"Xiaocong L, Yiping L (2021) P anoramic surround-view image generation method for ART. J Railway Sci Eng 01:46\u201354. https:\/\/doi.org\/10.19713\/j.cnki.43-1423\/u.t20200245","journal-title":"J Railway Sci Eng"},{"key":"10689_CR5","unstructured":"CHINA STATISTICAL YEARBOOK, http:\/\/www.stats.gov.cn\/tjsj\/ndsj."},{"key":"10689_CR6","doi-asserted-by":"publisher","unstructured":"Okuda, R., Kajiwara, Y., & Terashima, K. (2014, April). A survey of technical trend of ADAS and autonomous driving. In Technical Papers of 2014 International Symposium on VLSI Design, Automation and Test (pp. 1\u20134). IEEE. https:\/\/doi.org\/10.1109\/VLSI-DAT.2014.6834940.","DOI":"10.1109\/VLSI-DAT.2014.6834940"},{"key":"10689_CR7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.13889\/j.issn.2096-5427.2018.06.018","volume":"06","author":"L Tengjiao","year":"2018","unstructured":"Tengjiao L, Xiwen Y, Xiaoguang L, Xinrui Z (2018) Research on the dynamic mosaic method for perimeter image of multi-carriage articulated vehicle. Control Info Technol 06:104\u2013108. https:\/\/doi.org\/10.13889\/j.issn.2096-5427.2018.06.018","journal-title":"Control Info Technol"},{"key":"10689_CR8","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.19713\/j.cnki.43-1423\/u.t20210509","volume":"09","author":"L Yiping","year":"2021","unstructured":"Yiping L, Tian Y, Zhengliang W, Sisi L (2021) Recognition of vehicles based on sparse point cloud and image forautonomous rail rapid transit. J Railway Sci Eng 09:2444\u20132451. https:\/\/doi.org\/10.19713\/j.cnki.43-1423\/u.t20210509","journal-title":"J Railway Sci Eng"},{"key":"10689_CR9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.13889\/j.issn.2096-5427.2020.01.002","volume":"01","author":"H Yunqing","year":"2020","unstructured":"Yunqing H, Jianghua F, Teng L, Wenbo P, Xiwen Y, Jun L, Ruipeng H, Zhichao H (2020) Multi-source environment perception system for autonomous-rail rapid tram. Control Info Technol 01:13\u201318. https:\/\/doi.org\/10.13889\/j.issn.2096-5427.2020.01.002","journal-title":"Control Info Technol"},{"key":"10689_CR10","doi-asserted-by":"publisher","first-page":"67","DOI":"10.13889\/j.issn.2096-5427.2020.04.014","volume":"04","author":"L Teng","year":"2020","unstructured":"Teng L, Sisi L, Yunqing H, Xiaoguang L, Xiwen Y, Wenbo P, Yiping L, Wentian Y (2020) LiDAR-based Road Intrusion Detection Technology for Autonomous-rail Rapid Tram. Control Info Technol 04:67\u201372. https:\/\/doi.org\/10.13889\/j.issn.2096-5427.2020.04.014","journal-title":"Control Info Technol"},{"issue":"12","key":"10689_CR11","doi-asserted-by":"publisher","first-page":"23043","DOI":"10.1109\/TITS.2022.3194553","volume":"23","author":"S Liu","year":"2022","unstructured":"Liu S, Li C, Yuwen T, Wan Z, Luo Y (2022) A lightweight LiDAR-camera sensing method of obstacles detection and classification for autonomous rail rapid transit. IEEE Trans Intell Transp Syst 23(12):23043\u201323058. https:\/\/doi.org\/10.1109\/TITS.2022.3194553","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10689_CR12","doi-asserted-by":"publisher","first-page":"6187","DOI":"10.1007\/s00521-021-06800-2","volume":"34","author":"L Han","year":"2022","unstructured":"Han L, Zheng P, Li H et al (2022) A novel early warning strategy for right-turning blind zone based on vulnerable road users detection. Neural Comput & Applic 34:6187\u20136206. https:\/\/doi.org\/10.1007\/s00521-021-06800-2","journal-title":"Neural Comput & Applic"},{"issue":"8","key":"10689_CR13","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1177\/1477153515619777","volume":"48","author":"R Saraiji","year":"2016","unstructured":"Saraiji R, Younis D, Madi MT, Gibbons RB (2016) Pedestrian visibility at night: The effect of solid state streetlights. Light Res Technol 48(8):976\u2013991. https:\/\/doi.org\/10.1177\/1477153515619777","journal-title":"Light Res Technol"},{"key":"10689_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102765","volume":"68","author":"J Su","year":"2021","unstructured":"Su J, He X, Qing L, Niu T, Cheng Y, Peng Y (2021) A novel social distancing analysis in urban public space: a new online spatio-temporal trajectory approach. Sustain Cities Soc 68:102765. https:\/\/doi.org\/10.1016\/j.scs.2021.102765","journal-title":"Sustain Cities Soc"},{"issue":"9","key":"10689_CR15","doi-asserted-by":"publisher","first-page":"3756","DOI":"10.1109\/TITS.2019.2932802","volume":"21","author":"R Ojala","year":"2019","unstructured":"Ojala R, Veps\u00e4l\u00e4inen J, Hanhirova J, Hirvisalo V, Tammi K (2019) Novel convolutional neural network-based roadside unit for accurate pedestrian localisation. IEEE Trans Intell Transp Syst 21(9):3756\u20133765. https:\/\/doi.org\/10.1109\/TITS.2019.2932802","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10689_CR16","doi-asserted-by":"publisher","first-page":"10361","DOI":"10.1007\/s00521-023-08239-z","volume":"35","author":"C Yan","year":"2023","unstructured":"Yan C, Zhang H, Li X et al (2023) Cross-modality complementary information fusion for multispectral pedestrian detection. Neural Comput & Applic 35:10361\u201310386. https:\/\/doi.org\/10.1007\/s00521-023-08239-z","journal-title":"Neural Comput & Applic"},{"key":"10689_CR17","doi-asserted-by":"publisher","first-page":"12517","DOI":"10.1007\/s00521-021-06468-8","volume":"34","author":"G Li","year":"2022","unstructured":"Li G, Wang Q, Zuo C (2022) Emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network. Neural Comput Applic 34:12517\u201312526. https:\/\/doi.org\/10.1007\/s00521-021-06468-8","journal-title":"Neural Comput Applic"},{"key":"10689_CR18","doi-asserted-by":"publisher","unstructured":"Nanyan L, Jingyang Y. (2020). Research on Vehicle Detection Based on Visual Convolution Network Optimization. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-65955-4_17","DOI":"10.1007\/978-3-030-65955-4_17"},{"key":"10689_CR19","doi-asserted-by":"publisher","first-page":"23027","DOI":"10.1109\/ACCESS.2019.2899105","volume":"7","author":"I Yun","year":"2019","unstructured":"Yun I, Jung C, Wang X, Hero AO, Kim JK (2019) Part-level convolutional neural networks for pedestrian detection using saliency and boundary box alignment. IEEE Access 7:23027\u201323037. https:\/\/doi.org\/10.1109\/ACCESS.2019.2899105","journal-title":"IEEE Access"},{"key":"10689_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3287574","author":"WY Hsu","year":"2023","unstructured":"Hsu WY, Yang PY (2023) Pedestrian detection using multi-scale structure-enhanced super-resolution. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2023.3287574","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"10689_CR21","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1109\/TITS.2019.2963700","volume":"22","author":"P Yang","year":"2020","unstructured":"Yang P, Zhang G, Wang L, Xu L, Deng Q, Yang MH (2020) A part-aware multi-scale fully convolutional network for pedestrian detection. IEEE Trans Intell Transp Syst 22(2):1125\u20131137. https:\/\/doi.org\/10.1109\/TITS.2019.2963700","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10689_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114481","volume":"168","author":"WA Haque","year":"2021","unstructured":"Haque WA, Arefin S, Shihavuddin ASM, Hasan MA (2021) DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements. Expert Syst Appl 168:114481. https:\/\/doi.org\/10.1016\/j.eswa.2020.114481","journal-title":"Expert Syst Appl"},{"issue":"3","key":"10689_CR23","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.3390\/s22031193","volume":"22","author":"Z Charouh","year":"2022","unstructured":"Charouh Z, Ezzouhri A, Ghogho M, Guennoun Z (2022) A resource-efficient CNN-based method for moving vehicle detection. Sensors 22(3):1193. https:\/\/doi.org\/10.3390\/s22031193","journal-title":"Sensors"},{"issue":"12","key":"10689_CR24","doi-asserted-by":"publisher","first-page":"24454","DOI":"10.1109\/TITS.2022.3196854","volume":"23","author":"A Abdelmutalab","year":"2022","unstructured":"Abdelmutalab A, Wang C (2022) Pedestrian detection using MB-CSP model and boosted identity aware non-maximum suppression. IEEE Trans Intell Transp Syst 23(12):24454\u201324463. https:\/\/doi.org\/10.1109\/TITS.2022.3196854","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"23","key":"10689_CR25","doi-asserted-by":"publisher","first-page":"12476","DOI":"10.3390\/app122312476","volume":"12","author":"K Yi","year":"2022","unstructured":"Yi K, Luo K, Chen T, Hu R (2022) An improved YOLOX model and domain transfer strategy for nighttime pedestrian and vehicle detection. Appl Sci 12(23):12476. https:\/\/doi.org\/10.3390\/app122312476","journal-title":"Appl Sci"},{"issue":"3","key":"10689_CR26","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/TITS.2002.802932","volume":"3","author":"T Kato","year":"2002","unstructured":"Kato T, Ninomiya Y, Masaki I (2002) An obstacle detection method by fusion of radar and motion stereo. IEEE Trans Intell Transp Syst 3(3):182\u2013188. https:\/\/doi.org\/10.1109\/TITS.2002.802932","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10689_CR27","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.eswa.2019.06.048","volume":"136","author":"JVB Severino","year":"2019","unstructured":"Severino JVB, Zimmer A, Brandmeier T, Freire RZ (2019) Pedestrian recognition using micro Doppler effects of radar signals based on machine learning and multi-objective optimization. Expert Syst Appl 136:304\u2013315. https:\/\/doi.org\/10.1016\/j.eswa.2019.06.048","journal-title":"Expert Syst Appl"},{"key":"10689_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117837","volume":"206","author":"MF Kabir","year":"2022","unstructured":"Kabir MF, Roy S (2022) Real-time vehicular accident prevention system using deep learning architecture. Expert Syst Appl 206:117837. https:\/\/doi.org\/10.1016\/j.eswa.2022.117837","journal-title":"Expert Syst Appl"},{"key":"10689_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106604","volume":"124","author":"Z Zhou","year":"2023","unstructured":"Zhou Z, Fang Z, Wang J, Chen J, Li H, Han L, Zhang Z (2023) Driver vigilance detection based on deep learning with fused thermal image information for public transportation. Eng Appl Artif Intell 124:106604. https:\/\/doi.org\/10.1016\/j.engappai.2023.106604","journal-title":"Eng Appl Artif Intell"},{"issue":"13","key":"10689_CR30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S1474-6670(17)62003-2","volume":"16","author":"S Murakami","year":"1983","unstructured":"Murakami S (1983) Application of fuzzy controller to automobile speed control system. IFAC Proc Vol 16(13):43\u201348. https:\/\/doi.org\/10.1016\/S1474-6670(17)62003-2","journal-title":"IFAC Proc Vol"},{"issue":"2","key":"10689_CR31","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/87.987066","volume":"10","author":"M Druzhinina","year":"2002","unstructured":"Druzhinina M, Stefanopoulou AG, Moklegaard L (2002) Speed gradient approach to longitudinal control of heavy-duty vehicles equipped with variable compression brake. IEEE Trans Control Syst Technol 10(2):209\u2013220. https:\/\/doi.org\/10.1109\/87.987066","journal-title":"IEEE Trans Control Syst Technol"},{"issue":"1\u20134","key":"10689_CR32","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1504\/IJVD.2021.122253","volume":"86","author":"S Ming-lei","year":"2021","unstructured":"Ming-lei S, Ai-zeng L, Li-hua L (2021) Speed control of vehicle automatic driving system based on 5G vehicle network. Int J Veh Des 86(1\u20134):71\u201387. https:\/\/doi.org\/10.1504\/IJVD.2021.122253","journal-title":"Int J Veh Des"},{"key":"10689_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9562560","volume":"2021","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Zhang Y, Liu Z, Chen J, You T, Du C (2021) An eco-cruise control for electric vehicles moving on slope road with constant speed. J Adv Transp 2021:1\u201314. https:\/\/doi.org\/10.1155\/2021\/9562560","journal-title":"J Adv Transp"},{"key":"10689_CR34","doi-asserted-by":"publisher","first-page":"301","DOI":"10.19678\/j.issn.1000-3428.0047642","volume":"08","author":"DUAN Jianmin","year":"2018","unstructured":"Jianmin DUAN, Xiaosheng TIAN, Tian XIA, Xiaofeng HUA (2018) Trapezoidal speed planning method of intelligent vehicle based on intermediate speed. Comput Eng 08:301\u2013307+314. https:\/\/doi.org\/10.19678\/j.issn.1000-3428.0047642","journal-title":"Comput Eng"},{"key":"10689_CR35","doi-asserted-by":"publisher","first-page":"43","DOI":"10.13889\/j.issn.2096-5427.2020.01.007","volume":"01","author":"QIAN Hua","year":"2020","unstructured":"Hua QIAN, Jieren YU, Xiaofeng LUO, Jianchao FU, Wenfeng LIU, Yongqing XIE (2020) Brake system and its control strategy of autonomous-rail rapid tram. Control Info Technol 01:43\u201347. https:\/\/doi.org\/10.13889\/j.issn.2096-5427.2020.01.007","journal-title":"Control Info Technol"},{"key":"10689_CR36","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, and Farhadi A (2016) You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779\u2013788). https:\/\/doi.org\/10.48550\/arXiv.1506.02640.","DOI":"10.48550\/arXiv.1506.02640"},{"key":"10689_CR37","doi-asserted-by":"publisher","unstructured":"Redmon J, and Farhadi A (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263\u20137271). https:\/\/doi.org\/10.48550\/arXiv.1612.08242.","DOI":"10.48550\/arXiv.1612.08242"},{"key":"10689_CR38","doi-asserted-by":"publisher","unstructured":"Redmon J and Farhadi A (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. https:\/\/doi.org\/10.48550\/arXiv.1804.02767.","DOI":"10.48550\/arXiv.1804.02767"},{"key":"10689_CR39","doi-asserted-by":"publisher","unstructured":"Bochkovskiy A, Wang CY and Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https:\/\/doi.org\/10.48550\/arXiv.2004.10934.","DOI":"10.48550\/arXiv.2004.10934"},{"key":"10689_CR40","unstructured":"https:\/\/github.com\/ultralytics\/yolov5."},{"key":"10689_CR41","doi-asserted-by":"publisher","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L and Wei X (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976. https:\/\/doi.org\/10.48550\/arXiv.2209.02976.","DOI":"10.48550\/arXiv.2209.02976"},{"key":"10689_CR42","doi-asserted-by":"publisher","unstructured":"Wang CY, Bochkovskiy A and Liao HYM (2023). 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). https:\/\/doi.org\/10.48550\/arXiv.2207.02696.","DOI":"10.48550\/arXiv.2207.02696"},{"key":"10689_CR43","unstructured":"https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"10689_CR44","doi-asserted-by":"publisher","unstructured":"Liu S, Qi L Qin H, Shi J and Jia J (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759\u20138768). https:\/\/doi.org\/10.48550\/arXiv.1803.01534.","DOI":"10.48550\/arXiv.1803.01534"},{"key":"10689_CR45","doi-asserted-by":"publisher","unstructured":"Kulhandjian H, Barron J, Tamiyasu M, Thompson M and Kulhandjian M. (2023, February). Pedestrian Detection and Avoidance at Night Using Multiple Sensors and Machine Learning. In 2023 International Conference on Computing, Networking and Communications (ICNC) (pp. 165\u2013169). IEEE. https:\/\/doi.org\/10.1109\/ICNC57223.2023.10074081.","DOI":"10.1109\/ICNC57223.2023.10074081"},{"key":"10689_CR46","doi-asserted-by":"publisher","unstructured":"Liu W, Ren G, Yu R, Guo S, Zhu J and Zhang L (2022). Image-adaptive YOLO for object detection in adverse weather conditions. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 2, pp. 1792\u20131800). https:\/\/doi.org\/10.1609\/aaai.v36i2.20072.","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"10689_CR47","doi-asserted-by":"publisher","unstructured":"Sunkara R, Luo T (2023). No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13715. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-26409-2_27.","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"10689_CR48","doi-asserted-by":"publisher","unstructured":"Girshick R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440\u20131448). https:\/\/doi.org\/10.48550\/arXiv.1504.08083","DOI":"10.48550\/arXiv.1504.08083"},{"key":"10689_CR49","doi-asserted-by":"publisher","unstructured":"Yu J, Jiang Y, Wang Z, Cao Z and Huang T (2016). Unitbox: An advanced object detection network. In Proceedings of the 24th ACM international conference on Multimedia (pp. 516\u2013520). https:\/\/doi.org\/10.1145\/2964284.2967274.","DOI":"10.1145\/2964284.2967274"},{"key":"10689_CR50","doi-asserted-by":"publisher","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I and Savarese S (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 658\u2013666). https:\/\/doi.org\/10.48550\/arXiv.1902.09630.","DOI":"10.48550\/arXiv.1902.09630"},{"key":"10689_CR51","doi-asserted-by":"publisher","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R and Ren D (2020). Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 12993\u201313000). https:\/\/doi.org\/10.48550\/arXiv.1911.08287.","DOI":"10.48550\/arXiv.1911.08287"},{"key":"10689_CR52","doi-asserted-by":"publisher","unstructured":"He J, Erfani S, Ma X, Bailey J, Chi Y and Hua XS (2021). \u03b1-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression. Advances in Neural Information Processing Systems, 34, 20230\u201320242. https:\/\/doi.org\/10.48550\/arXiv.2110.13675.","DOI":"10.48550\/arXiv.2110.13675"},{"key":"10689_CR53","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"YF Zhang","year":"2022","unstructured":"Zhang YF, Ren W, Zhang Z, Jia Z, Wang L, Tan T (2022) Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506:146\u2013157. https:\/\/doi.org\/10.1016\/j.neucom.2022.07.042","journal-title":"Neurocomputing"},{"key":"10689_CR54","doi-asserted-by":"publisher","unstructured":"Gevorgyan Z (2022). SIoU loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740. https:\/\/doi.org\/10.48550\/arXiv.2205.12740.","DOI":"10.48550\/arXiv.2205.12740"},{"key":"10689_CR55","doi-asserted-by":"publisher","unstructured":"Tong Z, Chen Y, Xu Z and Yu R (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv preprint arXiv:2301.10051. https:\/\/doi.org\/10.48550\/arXiv.2301.10051.","DOI":"10.48550\/arXiv.2301.10051"},{"key":"10689_CR56","doi-asserted-by":"publisher","unstructured":"Wang J, Xu C, Yang W and Yu L (2021). A normalized Gaussian Wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389. https:\/\/doi.org\/10.48550\/arXiv.2110.13389.","DOI":"10.48550\/arXiv.2110.13389"},{"key":"10689_CR57","doi-asserted-by":"publisher","unstructured":"Stein GP, Mano O and Shashua A (2003). Vision-based ACC with a single camera: bounds on range and range rate accuracy. In IEEE IV2003 intelligent vehicles symposium. Proceedings (Cat. No. 03TH8683) (pp. 120\u2013125). IEEE. https:\/\/doi.org\/10.1109\/IVS.2003.1212895.","DOI":"10.1109\/IVS.2003.1212895"},{"issue":"11","key":"10689_CR58","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/34.888718","volume":"22","author":"Z Zhang","year":"2000","unstructured":"Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330\u20131334. https:\/\/doi.org\/10.1109\/34.888718","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10689_CR59","doi-asserted-by":"publisher","unstructured":"Han J, Liang X, Xu H, Chen K, Hong L, Mao J, ... and Xu C (2021). SODA10M: a large-scale 2D self\/Semi-supervised object detection dataset for autonomous driving. arXiv preprint arXiv:2106.11118. https:\/\/doi.org\/10.48550\/arXiv.2106.11118.","DOI":"10.48550\/arXiv.2106.11118"},{"key":"10689_CR60","doi-asserted-by":"publisher","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G ... and Chintala S (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32. https:\/\/doi.org\/10.48550\/arXiv.1912.01703.","DOI":"10.48550\/arXiv.1912.01703"},{"key":"10689_CR61","doi-asserted-by":"publisher","unstructured":"Yu F, Xian W, Chen Y, Liu F, Liao M, Madhavan V and Darrell T (2018). BDD100k: A diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687, 2(5), 6. https:\/\/doi.org\/10.48550\/arXiv.1805.04687.","DOI":"10.48550\/arXiv.1805.04687"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10689-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10689-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10689-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T22:02:29Z","timestamp":1738792949000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10689-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10689"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10689-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"1 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","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 conflict 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"}}]}}