{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:24:37Z","timestamp":1770459877086,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100017414","name":"Beijing Municipal Social Science Foundation","doi-asserted-by":"publisher","award":["24YTC038"],"award-info":[{"award-number":["24YTC038"]}],"id":[{"id":"10.13039\/100017414","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2023D01A57"],"award-info":[{"award-number":["2023D01A57"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72221002"],"award-info":[{"award-number":["72221002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017676","name":"Chunhui Project Foundation of the Education Department of China","doi-asserted-by":"publisher","award":["HZKY20220053"],"award-info":[{"award-number":["HZKY20220053"]}],"id":[{"id":"10.13039\/501100017676","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11554-025-01799-4","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T06:05:12Z","timestamp":1763359512000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PIDNet-LW: lightweight semantic segmentation for edge devices"],"prefix":"10.1007","volume":"23","author":[{"given":"Siyuan","family":"Qian","sequence":"first","affiliation":[]},{"given":"Dongfang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhentong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Hailong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhihao","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"issue":"531","key":"1799_CR1","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1080\/01621459.2019.1665526","volume":"115","author":"Y Guan","year":"2020","unstructured":"Guan, Y., Johnson, M.C., Katzfuss, M., Mannshardt, E., Messier, K.P., Reich, B.J., Song, J.J.: Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles. J. Am. Stat. Assoc. 115(531), 1111\u20131124 (2020)","journal-title":"J. Am. Stat. Assoc."},{"key":"1799_CR2","doi-asserted-by":"crossref","unstructured":"Salem, M., Gomaa, A., Tsurusaki, N.: Detection of earthquake-induced building damages using remote sensing data and deep learning: a case study of Mashiki town, Japan. In: IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 2350\u20132353. IEEE, Pasadena, CA, USA (2023)","DOI":"10.1109\/IGARSS52108.2023.10282550"},{"key":"1799_CR3","doi-asserted-by":"crossref","unstructured":"Gomaa, A.: Advanced domain adaptation technique for object detection leveraging semi-automated dataset construction and enhanced YOLOv8. In: 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES), pp. 211\u2013214. IEEE, Giza, Egypt (2024)","DOI":"10.1109\/NILES63360.2024.10753164"},{"issue":"35","key":"1799_CR4","doi-asserted-by":"publisher","first-page":"26023","DOI":"10.1007\/s11042-020-09242-5","volume":"79","author":"A Gomaa","year":"2020","unstructured":"Gomaa, A., Abdelwahab, M.M., Abo-Zahhad, M.: Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis. Multimed. Tools Appl. 79(35), 26023\u201326043 (2020)","journal-title":"Multimed. Tools Appl."},{"key":"1799_CR5","doi-asserted-by":"crossref","unstructured":"Gomaa, A., Abdelwahab, M.M., Abo-Zahhad, M.: Real-time algorithm for simultaneous vehicle detection and tracking in aerial view videos. In: 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 222\u2013225. IEEE, Giza, Egypt (2018)","DOI":"10.1109\/MWSCAS.2018.8624022"},{"key":"1799_CR6","doi-asserted-by":"crossref","unstructured":"Yuan, B., Zhao, D.: A survey on continual semantic segmentation: theory, challenge, method and application. IEEE Trans. Pattern Anal. Mach. Intell. 46, 10891\u201310910 (2024)","DOI":"10.1109\/TPAMI.2024.3446949"},{"issue":"2","key":"1799_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.nexres.2024.100076","volume":"1","author":"A Paul","year":"2024","unstructured":"Paul, A., Machavaram, R.: Greenhouse capsicum detection in thermal imaging: a comparative analysis of a single-shot and a novel zero-shot detector. Next Res. 1(2), 100076 (2024)","journal-title":"Next Res."},{"key":"1799_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.fraope.2025.100243","volume":"10","author":"A Paul","year":"2025","unstructured":"Paul, A., Machavaram, R.: Advancing capsicum detection in night-time greenhouse environments using deep learning models: comparative analysis and improved zero-shot detection through fusion with a single-shot detector. Frankl. Open 10, 100243 (2025)","journal-title":"Frankl. Open"},{"issue":"1","key":"1799_CR9","doi-asserted-by":"publisher","first-page":"2437162","DOI":"10.1080\/21642583.2024.2437162","volume":"12","author":"A Paul","year":"2024","unstructured":"Paul, A., Machavaram, R.: Advanced segmentation models for automated capsicum peduncle detection in night-time greenhouse environments. Syst. Sci. Control Eng. 12(1), 2437162 (2024)","journal-title":"Syst. Sci. Control Eng."},{"key":"1799_CR10","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. In: Advances in Neural Information Processing Systems, vol. 37 (2024)"},{"key":"1799_CR11","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., Bhattacharyya, S.P.: PIDNet: a real-time semantic segmentation network inspired by PID controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Vancouver, BC, Canada, pp. 19529\u201319539 (2023)","DOI":"10.1109\/CVPR52729.2023.01871"},{"issue":"2","key":"1799_CR12","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1007\/s11263-010-0344-8","volume":"95","author":"G Csurka","year":"2011","unstructured":"Csurka, G., Perronnin, F.: An efficient approach to semantic segmentation. Int. J. Comput. Vis. 95(2), 198\u2013212 (2011)","journal-title":"Int. J. Comput. Vis."},{"key":"1799_CR13","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, MA, USA,  pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1799_CR14","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, IEEE, Las Vegas, NV, USA, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1799_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, HI, USA, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"1799_CR16","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, HI, USA, pp. 1925\u20131934 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"1799_CR17","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","volume":"30","author":"T Wu","year":"2020","unstructured":"Wu, T., Tang, S., Zhang, R., Cao, J., Zhang, Y.: CGNet: a light-weight context guided network for semantic segmentation. IEEE Trans. Image Process. 30, 1169\u20131179 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1799_CR18","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), Springer, Munich, Germany, pp. 325\u2013341 (2018)","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"1799_CR19","doi-asserted-by":"crossref","unstructured":"Goel, A., Katiyar, A., Goel, A.K., Kumar, A.: Comparative study of ANN, CNN, and RNN hardware chips. Natl. Acad. Sci. Lett. 1\u20137 (2025) (Online First)","DOI":"10.1007\/s40009-025-01670-x"},{"issue":"4","key":"1799_CR20","doi-asserted-by":"publisher","first-page":"495","DOI":"10.11591\/ijra.v13i4.pp495-505","volume":"13","author":"A Pant","year":"2024","unstructured":"Pant, A., Kumar, A.: Design and implementation of deep neural network hardware chip and its performance analysis. IAES Int. J. Robot. Autom. (IJRA) 13(4), 495\u2013505 (2024)","journal-title":"IAES Int. J. Robot. Autom. (IJRA)"},{"key":"1799_CR21","doi-asserted-by":"crossref","unstructured":"Kumar, M., Gupta, M., Kumar, A.: CNN-embedded hardware chip design and simulation. In: Embedded Devices and Internet of Things, pp. 21\u201339. CRC Press, Boca Raton (2024)","DOI":"10.1201\/9781003510420-2"},{"key":"1799_CR22","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient convolutional neural networks for mobile vision applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, HI, USA, pp. 2704-2710 (2017)"},{"key":"1799_CR23","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,  IEEE, Honolulu, HI, USA, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1799_CR24","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: A deep neural network architecture for real-time semantic segmentation. In: Proceedings of the British Machine Vision Conference, pp. 267\u2013278 (2016)"},{"issue":"12","key":"1799_CR25","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1799_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European Conference on Computer Vision (ECCV), Springer, Munich, Germany, pp. 405\u2013420 (2018)","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"1799_CR27","doi-asserted-by":"crossref","unstructured":"Nigam, I., Huang, C., Ramanan, D.: Ensemble knowledge transfer for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1499\u20131508. IEEE, Lake Tahoe, NV, USA (2018)","DOI":"10.1109\/WACV.2018.00168"},{"key":"1799_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,  IEEE, New Orleans, LA, USA, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1799_CR29","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"1799_CR30","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), Springer, Munich, Germany, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"2","key":"1799_CR31","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3390\/rs15020379","volume":"15","author":"Y Li","year":"2023","unstructured":"Li, Y., Cheng, Z., Wang, C., Zhao, J., Huang, L.: RCCT-ASPPNet: dual-encoder remote image segmentation based on transformer and ASPP. Remote Sens. 15(2), 379 (2023)","journal-title":"Remote Sens."},{"key":"1799_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111035","volume":"150","author":"Q Zheng","year":"2024","unstructured":"Zheng, Q., Xu, L., Wang, F., Xu, Y., Lin, C., Zhang, G.: HilbertSCNet: self-attention networks for small target segmentation of aerial drone images. Appl. Soft Comput. 150, 111035 (2024)","journal-title":"Appl. Soft Comput."},{"key":"1799_CR33","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, D., Ponce, J., Ham, B.: SFNet: learning object-aware semantic correspondence. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,  IEEE, Long Beach, CA, USA, pp. 2278\u20132287 (2019)","DOI":"10.1109\/CVPR.2019.00238"},{"key":"1799_CR34","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1799_CR35","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wu, D., Yu, C., Chu, X., Sang, N., Gao, C.:  Sctnet: Single-branch cnn with transformer semantic information for real-time segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 6, pp. 6378\u20136386 (2024)","DOI":"10.1609\/aaai.v38i6.28457"},{"key":"1799_CR36","unstructured":"Wan, Q., Huang, Z., Lu, J., Yu, G., Zhang, L.: SeaFormer: squeeze-enhanced axial transformer for mobile semantic segmentation. In: The Eleventh International Conference on Learning Representations, OpenReview, Kigali, Rwanda (2023)"},{"key":"1799_CR37","doi-asserted-by":"crossref","unstructured":"Kerdvibulvech, C.: Multimodal AI model for zero-shot vehicle brand identification. Multimed. Tools Appl.  84, 33125\u201333144 (2025)","DOI":"10.1007\/s11042-024-20559-3"},{"key":"1799_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2024.112485","volume":"166","author":"X Li","year":"2024","unstructured":"Li, X., Zhang, X., Jin, X.: Spatio-temporal characteristics and driving factors of cultivated land change in various agricultural regions of China: a detailed analysis based on county-level data. Ecol. Ind. 166, 112485 (2024)","journal-title":"Ecol. Ind."},{"key":"1799_CR39","doi-asserted-by":"crossref","unstructured":"Karnehm, D., Samanta, A., Rosenm\u00fcller, C., Neve, A., Williamson, S.: Core temperature estimation of lithium-ion batteries using long short-term memory (LSTM) network and Kolmogorov\u2013Arnold network (KAN). IEEE Trans. Transp. Electrif. 11, 10391\u201310401 (2025)","DOI":"10.1109\/TTE.2025.3559633"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01799-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01799-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01799-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:48:29Z","timestamp":1770396509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01799-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1799"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01799-4","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]},"assertion":[{"value":"26 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","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 there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"8"}}