{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:41:42Z","timestamp":1772300502718,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Hebei Provincial Department of education in 2021 provincial postgraduate demonstration course project construction","award":["KCJSX2021024"],"award-info":[{"award-number":["KCJSX2021024"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s00521-022-06932-z","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T04:34:59Z","timestamp":1642998899000},"page":"3573-3587","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["LAANet: lightweight attention-guided asymmetric network for real-time semantic segmentation"],"prefix":"10.1007","volume":"34","author":[{"given":"Xiuling","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Bingce","family":"Du","sequence":"additional","affiliation":[]},{"given":"Ziyun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Tingbo","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"6932_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.robot.2018.02.017","volume":"104","author":"B Bovcon","year":"2018","unstructured":"Bovcon B, Mandeljc R, Per\u0161 J et al (2018) Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation. Robot Auton Syst 104:1\u201313","journal-title":"Robot Auton Syst"},{"issue":"2","key":"6932_CR2","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1109\/TII.2018.2849348","volume":"15","author":"X Zhang","year":"2019","unstructured":"Zhang X, Chen Z, Wu QMJ et al (2019) Fast semantic segmentation for scene perception. IEEE Trans Ind Inf 15(2):1183\u20131192","journal-title":"IEEE Trans Ind Inf"},{"key":"6932_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2021.3059968","volume":"99","author":"S Minaee","year":"2021","unstructured":"Minaee S, Boykov Y, Porikli F et al (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 99:1\u20131","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6932_CR4","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"6932_CR5","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E (2016) ENet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147"},{"issue":"1","key":"6932_CR6","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","volume":"19","author":"E Romera","year":"2018","unstructured":"Romera E, Alvarez JM, Bergasa LM, Arroyo R (2018) ERFNet: efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans Intell Transp Syst 19(1):263\u2013272","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6932_CR7","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"6932_CR8","unstructured":"Li G, Y un I, Kim J, Kim J (2019) DABNet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357"},{"key":"6932_CR9","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon I.S (2018) CBAM: Convolutional Block Attention Module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"6932_CR10","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X et al (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 6230\u20136239","DOI":"10.1109\/CVPR.2017.660"},{"issue":"4","key":"6932_CR11","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2017) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6932_CR12","doi-asserted-by":"crossref","unstructured":"Emara T, Abd El Munim HE, Abbas HM (2019) LiteSeg: a novel lightweight ConvNet for semantic segmentation. Dig Image Comput Tech Appl (DICTA), pp 1\u20137","DOI":"10.1109\/DICTA47822.2019.8945975"},{"key":"6932_CR13","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhou Q, Liu J et al (2019) LEDNet: A lightweight encoder-decoder network for real-time semantic segmentation. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 1860\u20131864","DOI":"10.1109\/ICIP.2019.8803154"},{"key":"6932_CR14","doi-asserted-by":"crossref","unstructured":"Li H, Xiong P, Fan H, Sun J (2019) DFANet: Deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 9514\u20139523","DOI":"10.1109\/CVPR.2019.00975"},{"key":"6932_CR15","doi-asserted-by":"crossref","unstructured":"Liu J, Zhou Q, Qiang Y et al (2020) FDDWNet: A lightweight convolutional neural network for real-time semantic segmentation. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2373\u20132377","DOI":"10.1109\/ICASSP40776.2020.9053838"},{"key":"6932_CR16","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/LSP.2021.3051845","volume":"28","author":"Y Li","year":"2021","unstructured":"Li Y, Li X, Xiao C et al (2021) EACNet: enhanced asymmetric convolution for real-time semantic segmentation. IEEE Signal Proces Lett 28:234\u2013238","journal-title":"IEEE Signal Proces Lett"},{"key":"6932_CR17","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S et al (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"6932_CR18","doi-asserted-by":"crossref","unstructured":"Brostow GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: Proceedings of the European conference on computer vision (ECCV), pp 44\u201357","DOI":"10.1007\/978-3-540-88682-2_5"},{"key":"6932_CR19","doi-asserted-by":"crossref","unstructured":"Lou A, Loew M (2021) CFPNet: channel-wise feature pyramid for real-time semantic segmentation. arXiv preprint arXiv:2103.12212","DOI":"10.1109\/ICIP42928.2021.9506485"},{"issue":"6","key":"6932_CR20","doi-asserted-by":"publisher","first-page":"3258","DOI":"10.1109\/TITS.2020.2980426","volume":"22","author":"G Dong","year":"2021","unstructured":"Dong G, Yan Y, Shen C, Wang H (2021) Real-time high performance semantic image segmentation of urban street scenes. IEEE Trans Intell Transp Syst 22(6):3258\u20133274","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6932_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02437-9","author":"XL Zhang","year":"2021","unstructured":"Zhang XL, Du BC, Luo ZC et al (2021) Lightweight and efficient asymmetric network design for real-time semantic segmentation. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02437-9","journal-title":"Appl Intell"},{"key":"6932_CR22","doi-asserted-by":"crossref","unstructured":"Lo SY , Hang HM , Chan SW et al (2018) Efficient dense modules of asymmetric convolution for real-time semantic segmentation. arXiv preprint arXiv:1809.06323","DOI":"10.1145\/3338533.3366558"},{"key":"6932_CR23","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhou Q, Wu X (2019) ESNet: An efficient symmetric network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 41\u201352","DOI":"10.1007\/978-3-030-31723-2_4"},{"key":"6932_CR24","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Caspi A et al (2018) ESPNet: Efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 561\u2013580","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"6932_CR25","doi-asserted-by":"publisher","first-page":"5175","DOI":"10.1109\/TIP.2020.2976856","volume":"29","author":"Z Yang","year":"2020","unstructured":"Yang Z, Yu H, Feng M et al (2020) Small object augmentation of urban scenes for real-time semantic segmentation. IEEE Trans Image Process 29:5175\u20135190","journal-title":"IEEE Trans Image Process"},{"key":"6932_CR26","unstructured":"Sun B, Li J, Shao M et al (2019) LPRNet: lightweight deep network by low-rank pointwise residual convolution. arXiv preprint arXiv:1910.11853"},{"key":"6932_CR27","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) ESPNetv2: a light-weight, power efficient, and general purpose convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 9182-9192","DOI":"10.1109\/CVPR.2019.00941"},{"key":"6932_CR28","doi-asserted-by":"crossref","unstructured":"Jiang W, Xie Z, Li Y et al (2020) LRNNet: A light-weighted network with efficient reduced non-local operation for real-time semantic segmentation. arXiv preprint arXiv:2006.02706","DOI":"10.1109\/ICMEW46912.2020.9106038"},{"key":"6932_CR29","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Gao C et al (2020) Context prior for scene segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 12413\u201312422","DOI":"10.1109\/CVPR42600.2020.01243"},{"issue":"4","key":"6932_CR30","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10489-019-01587-1","volume":"50","author":"J Wang","year":"2020","unstructured":"Wang J, Xiong H, Wang H, Nian X (2020) ADSCNEt: asymmetric depthwise separable convolution for semantic segmentation in real-time. Appl Intell 50(4):1045\u20131056","journal-title":"Appl Intell"},{"key":"6932_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3098355","author":"G Gao","year":"2021","unstructured":"Gao G, Xu G, Yu Y et al (2021) MSCFNet: a lightweight network with multi-scale context fusion for real-time semantic segmentation. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2021.3098355","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6932_CR32","doi-asserted-by":"crossref","unstructured":"Yang Q, Chen T, Fan J et al (2021) EADNet: efficient asymmetric dilated network for semantic segmentation. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2315\u20132319","DOI":"10.1109\/ICASSP39728.2021.9413767"},{"key":"6932_CR33","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3141\u20133149","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"2","key":"6932_CR34","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1109\/TITS.2019.2962094","volume":"22","author":"HY Han","year":"2021","unstructured":"Han HY, Chen YC, Hsiao PY, Fu LC (2021) Using channel-wise attention for deep CNN based real-time semantic segmentation with class-aware edge information. IEEE Trans Intell Transp Syst 22(2):1041\u20131051","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6932_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107940","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Sun X, Dong J et al (2021) GPNet: gated pyramid network for semantic segmentation. Pattern Recogn. https:\/\/doi.org\/10.1016\/j.patcog.2021.107940","journal-title":"Pattern Recogn"},{"key":"6932_CR36","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neunet.2021.01.021","volume":"137","author":"C Peng","year":"2021","unstructured":"Peng C, Tian T, Chen C et al (2021) Bilateral attention decoder: a lightweight decoder for real-time semantic segmentation. Neural Networks 137:188\u2013199","journal-title":"Neural Networks"},{"key":"6932_CR37","doi-asserted-by":"crossref","unstructured":"Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3080\u20133089","DOI":"10.1109\/CVPR.2019.00320"},{"issue":"13","key":"6932_CR38","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1049\/el.2020.0318","volume":"56","author":"L Yin","year":"2020","unstructured":"Yin L, Hu H (2020) Enhanced global attention upsample decoder based on enhanced spatial attention and feature aggregation module for semantic segmentation. Electron Lett 56(13):659\u2013661","journal-title":"Electron Lett"},{"key":"6932_CR39","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","volume":"30","author":"T Wu","year":"2021","unstructured":"Wu T, Tang S, Zhang R et al (2021) CGNet: a light-weight context guided network for semantic segmentation. IEEE Trans Image Process 30:1169\u20131179","journal-title":"IEEE Trans Image Process"},{"key":"6932_CR40","doi-asserted-by":"crossref","unstructured":"Wang L, Xu Q, Xiong Z et al (2019) A multi-level feature fusion network for real-time semantic segmentation. In: Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP), pp 1\u20136","DOI":"10.1109\/WCSP.2019.8927880"},{"key":"6932_CR41","unstructured":"Liu M, Yin H (2019) Feature pyramid encoding network for real-time semantic segmentation. arXiv preprint arXiv:1909.08599"},{"key":"6932_CR42","doi-asserted-by":"crossref","unstructured":"Liu C, Gao H, Chen A (2020) A real-time semantic segmentation algorithm based on improved lightweight network. In: Proceedings of the International Symposium on Autonomous Systems (ISAS), pp 249\u2013253","DOI":"10.1109\/ISAS49493.2020.9378857"},{"key":"6932_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02446-8","author":"X Hu","year":"2021","unstructured":"Hu X, Jing L, Sehar U (2021) Joint pyramid attention network for real-time semantic segmentation of urban scenes. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02446-8","journal-title":"Appl Intell"},{"key":"6932_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3044672","author":"Q Lv","year":"2021","unstructured":"Lv Q, Sun X, Chen C et al (2021) Parallel complement network for real-time semantic segmentation of road scenes. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2020.3044672","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6932_CR45","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Peng C et al (2018) BiSeNet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 334\u2013349","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"6932_CR46","doi-asserted-by":"crossref","unstructured":"Yu C, Gao C, Wang J, et al (2020) BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation. arXiv preprint arXiv:2004.02147","DOI":"10.1007\/s11263-021-01515-2"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06932-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-06932-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06932-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T06:42:54Z","timestamp":1645684974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-06932-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,24]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["6932"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-06932-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,24]]},"assertion":[{"value":"13 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}