{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:55:03Z","timestamp":1757451303282,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Zhejiang Provincial Public Welfare Technology Application Research Project","award":["LGG22F030016"],"award-info":[{"award-number":["LGG22F030016"]}]},{"name":"Postgraduate Research and Innovation Project of Huzhou University","award":["2023KYCX57"],"award-info":[{"award-number":["2023KYCX57"]}]},{"name":"Huzhou City Public Welfare Technology Application Research Project","award":["2022GZ10"],"award-info":[{"award-number":["2022GZ10"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,19]]},"DOI":"10.1145\/3653781.3653800","type":"proceedings-article","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T12:22:26Z","timestamp":1717244546000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Road Unexpected Obstacle Segmentation Algorithm Based on Improved RFB Deeplabv3+"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4880-6224","authenticated-orcid":false,"given":"Jiayin","family":"Xuan","sequence":"first","affiliation":[{"name":"School of Engineering Hu zhou university, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0248-637X","authenticated-orcid":false,"given":"Wenyan","family":"Ci","sequence":"additional","affiliation":[{"name":"School of Engineering Hu zhou university, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1057-5648","authenticated-orcid":false,"given":"Yangxun","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Engineering Hu zhou university, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3265-2446","authenticated-orcid":false,"given":"Lu","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Engineering Hu zhou university, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2015.10.033"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2198214"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00423110600563338"},{"key":"e_1_3_2_1_4_1","first-page":"2190","volume-title":"Conf. on Comput. Vis.","author":"Kontschieder P.","year":"2011","unstructured":"P. Kontschieder, S. R. Bulo, H. Bischof, and M. Pelillo, \"Structured class-labels in random forests for semantic image labelling,\" in 2011 Int. Conf. on Comput. Vis., Barcelona, Spain, 06-13 Nov. 2011, pp. 2190-2197."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_2_1_8_1","volume-title":"Very deep convolutional networks for large-scale image recognition,\" arXiv: 1409.1556","author":"Simonyan K.","year":"2014","unstructured":"K. Simonyan and A. Zisserman, \"Very deep convolutional networks for large-scale image recognition,\" arXiv: 1409.1556, 2014."},{"key":"e_1_3_2_1_9_1","first-page":"1","volume-title":"Comput. Vis. and Pattern Recognit.","author":"Szegedy C.","year":"2015","unstructured":"C. Szegedy , \"Going deeper with convolutions,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Boston, MA, USA, 7-12 Jun. 2015, pp. 1-9."},{"key":"e_1_3_2_1_10_1","first-page":"770","article-title":"Deep residual learning for image recognition,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Las Vegas","volume":"27","author":"He K.","year":"2016","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \"Deep residual learning for image recognition,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Las Vegas, NV, USA, 27-30 Jun. 2016, pp. 770-778.","journal-title":"NV, USA"},{"key":"e_1_3_2_1_11_1","first-page":"3431","volume-title":"Comput. Vis. and Pattern Recognit.","author":"Long J.","year":"2015","unstructured":"J. Long, E. Shelhamer, and T. Darrell, \"Fully convolutional networks for semantic segmentation,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Boston, MA, USA, 27-30 Jun. 2015, pp. 3431-3440."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_2_1_13_1","volume-title":"Enet: A deep neural network architecture for real-time semantic segmentation,\" arXiv: 1606.02147","author":"Paszke A.","year":"2016","unstructured":"A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, \"Enet: A deep neural network architecture for real-time semantic segmentation,\" arXiv: 1606.02147, 2016."},{"key":"e_1_3_2_1_14_1","volume-title":"Semantic image segmentation with deep convolutional nets and fully connected crfs,\" arXiv: 1412.7062","author":"Chen L.-C.","year":"2014","unstructured":"L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, \"Semantic image segmentation with deep convolutional nets and fully connected crfs,\" arXiv: 1412.7062, 2014."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_2_1_16_1","volume-title":"Rethinking atrous convolution for semantic image segmentation,\" arXiv: 1706.05587","author":"Chen L.-C.","year":"2017","unstructured":"L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, \"Rethinking atrous convolution for semantic image segmentation,\" arXiv: 1706.05587, 2017."},{"key":"e_1_3_2_1_17_1","first-page":"801","volume-title":"Conf. Comput. Vis.","author":"Chen L.-C.","year":"2018","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, \"Encoder-decoder with atrous separable convolution for semantic image segmentation,\" in Eur. Conf. Comput. Vis., Munich, Germany, 08-14 Sep. 2018, pp. 801-818."},{"key":"e_1_3_2_1_18_1","first-page":"385","volume-title":"Conf. Comput. Vis.","author":"Liu S.","year":"2018","unstructured":"S. Liu and D. Huang, \"Receptive field block net for accurate and fast object detection,\" in Eur. Conf. Comput. Vis., Munich, Germany, 08-14 Sep. 2018, pp. 385-400."},{"key":"e_1_3_2_1_19_1","first-page":"4510","volume-title":"USA","author":"Sandler M.","year":"2018","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, \"Mobilenetv2: Inverted residuals and linear bottlenecks,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Salt Lake City, UT, USA, 18-23 Jun. 2018, pp. 4510-4520."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_1_21_1","first-page":"31","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning,\" in AAAI Conf. Artif. Intell., San Francisco","volume":"4","author":"Szegedy C.","year":"2017","unstructured":"C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, \"Inception-v4, inception-resnet and the impact of residual connections on learning,\" in AAAI Conf. Artif. Intell., San Francisco, California USA, 4-9 Feb. 2017, vol. 31, no. 1.","journal-title":"California USA"},{"key":"e_1_3_2_1_22_1","first-page":"21","volume-title":"Conf. Comput. Vis.","author":"Liu W.","year":"2016","unstructured":"W. Liu , \"Ssd: Single shot multibox detector,\" in Eur. Conf. Comput. Vis., Amsterdam, The Netherlands, 11-14 Oct. 2016, pp. 21-37."},{"key":"e_1_3_2_1_23_1","volume-title":"Neural Inf. Process. Syst.","volume":"29","author":"Dai J.","year":"2016","unstructured":"J. Dai, Y. Li, K. He, and J. Sun, \"R-fcn: Object detection via region-based fully convolutional networks,\" Adv. Neural Inf. Process. Syst., vol. 29, 2016."},{"key":"e_1_3_2_1_24_1","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Honolulu","volume":"21","author":"Chollet F.","year":"2017","unstructured":"F. Chollet, \"Xception: Deep learning with depthwise separable convolutions,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Honolulu, HI, USA, 21-26 Jul. 2017, pp. 1251-1258.","journal-title":"HI, USA"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods11243999"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759186"},{"key":"e_1_3_2_1_27_1","first-page":"2881","article-title":"Pyramid scene parsing network,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Honolulu","volume":"21","author":"Zhao H.","year":"2017","unstructured":"H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, \"Pyramid scene parsing network,\" in IEEE Conf. Comput. Vis. and Pattern Recognit., Honolulu, HI, USA, 21-26 Jul. 2017, pp. 2881-2890.","journal-title":"HI, USA"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12188937"}],"event":{"name":"CVDL 2024: The International Conference on Computer Vision and Deep Learning","acronym":"CVDL 2024","location":"Changsha China"},"container-title":["Proceedings of the International Conference on Computer Vision and Deep Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653781.3653800","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3653781.3653800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:28:31Z","timestamp":1755876511000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653781.3653800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,19]]},"references-count":28,"alternative-id":["10.1145\/3653781.3653800","10.1145\/3653804"],"URL":"https:\/\/doi.org\/10.1145\/3653781.3653800","relation":{},"subject":[],"published":{"date-parts":[[2024,1,19]]},"assertion":[{"value":"2024-06-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}