{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:09:55Z","timestamp":1750219795111,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,23]]},"DOI":"10.1145\/3579109.3579110","type":"proceedings-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T07:46:29Z","timestamp":1678779989000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmentation Comparison with FCN Based approach and YOLO Based approach"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5823-3323","authenticated-orcid":false,"given":"Tianren","family":"Zhang","sequence":"first","affiliation":[{"name":"Hebei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7047-1164","authenticated-orcid":false,"given":"Yancong","family":"Deng","sequence":"additional","affiliation":[{"name":"university of california San Diego, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Shivang Agarwal Jean\u00a0Ogier du Terrail and Fr\u00e9d\u00e9ric Jurie. 2018. Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks. CoRR abs\/1809.03193(2018). http:\/\/arxiv.org\/abs\/1809.03193  Shivang Agarwal Jean\u00a0Ogier du Terrail and Fr\u00e9d\u00e9ric Jurie. 2018. Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks. CoRR abs\/1809.03193(2018). http:\/\/arxiv.org\/abs\/1809.03193"},{"key":"e_1_3_2_1_2_1","unstructured":"Alexey Bochkovskiy Chien-Yao Wang and Hong-Yuan\u00a0Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. CoRR abs\/2004.10934(2020). https:\/\/arxiv.org\/abs\/2004.10934  Alexey Bochkovskiy Chien-Yao Wang and Hong-Yuan\u00a0Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. CoRR abs\/2004.10934(2020). https:\/\/arxiv.org\/abs\/2004.10934"},{"key":"e_1_3_2_1_3_1","volume-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","author":"Chen Liang-Chieh","year":"2017","unstructured":"Liang-Chieh Chen , George Papandreou , Iasonas Kokkinos , Kevin Murphy , and Alan\u00a0 L Yuille . 2017 . Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs . IEEE transactions on pattern analysis and machine intelligence 40, 4(2017), 834\u2013848. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan\u00a0L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4(2017), 834\u2013848."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_1_5_1","volume-title":"Journal of Physics: Conference Series, Vol.\u00a01004","author":"Juan Du.","year":"2029","unstructured":"Juan Du. 2018. Understanding of object detection based on CNN family and YOLO . In Journal of Physics: Conference Series, Vol.\u00a01004 . IOP Publishing , 01 2029 . Juan Du. 2018. Understanding of object detection based on CNN family and YOLO. In Journal of Physics: Conference Series, Vol.\u00a01004. IOP Publishing, 012029."},{"key":"e_1_3_2_1_6_1","unstructured":"Alberto Garcia-Garcia Sergio Orts-Escolano Sergiu Oprea Victor Villena-Martinez and Jos\u00e9\u00a0Garc\u00eda Rodr\u00edguez. 2017. A Review on Deep Learning Techniques Applied to Semantic Segmentation. CoRR abs\/1704.06857(2017). http:\/\/arxiv.org\/abs\/1704.06857  Alberto Garcia-Garcia Sergio Orts-Escolano Sergiu Oprea Victor Villena-Martinez and Jos\u00e9\u00a0Garc\u00eda Rodr\u00edguez. 2017. A Review on Deep Learning Techniques Applied to Semantic Segmentation. CoRR abs\/1704.06857(2017). http:\/\/arxiv.org\/abs\/1704.06857"},{"key":"e_1_3_2_1_7_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385(2015). http:\/\/arxiv.org\/abs\/1512.03385  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385(2015). http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_3_2_1_8_1","unstructured":"Jie Hu Li Shen and Gang Sun. 2017. Squeeze-and-Excitation Networks. CoRR abs\/1709.01507(2017). http:\/\/arxiv.org\/abs\/1709.01507  Jie Hu Li Shen and Gang Sun. 2017. Squeeze-and-Excitation Networks. CoRR abs\/1709.01507(2017). http:\/\/arxiv.org\/abs\/1709.01507"},{"key":"e_1_3_2_1_9_1","unstructured":"Petr Hurt\u00edk Vojtech Molek Jan Hula Marek Vajgl Pavel Vlas\u00e1nek and Tomas Nejezchleba. 2020. Poly-YOLO: higher speed more precise detection and instance segmentation for YOLOv3. CoRR abs\/2005.13243(2020). https:\/\/arxiv.org\/abs\/2005.13243  Petr Hurt\u00edk Vojtech Molek Jan Hula Marek Vajgl Pavel Vlas\u00e1nek and Tomas Nejezchleba. 2020. Poly-YOLO: higher speed more precise detection and instance segmentation for YOLOv3. CoRR abs\/2005.13243(2020). https:\/\/arxiv.org\/abs\/2005.13243"},{"key":"e_1_3_2_1_10_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey\u00a0 E Hinton . 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 ( 2012 ). Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_1_11_1","unstructured":"Jonathan Long Evan Shelhamer and Trevor Darrell. 2014. Fully Convolutional Networks for Semantic Segmentation. CoRR abs\/1411.4038(2014). http:\/\/arxiv.org\/abs\/1411.4038  Jonathan Long Evan Shelhamer and Trevor Darrell. 2014. Fully Convolutional Networks for Semantic Segmentation. CoRR abs\/1411.4038(2014). http:\/\/arxiv.org\/abs\/1411.4038"},{"key":"e_1_3_2_1_12_1","unstructured":"Joseph Redmon Santosh\u00a0Kumar Divvala Ross\u00a0B. Girshick and Ali Farhadi. 2015. You Only Look Once: Unified Real-Time Object Detection. CoRR abs\/1506.02640(2015). http:\/\/arxiv.org\/abs\/1506.02640  Joseph Redmon Santosh\u00a0Kumar Divvala Ross\u00a0B. Girshick and Ali Farhadi. 2015. You Only Look Once: Unified Real-Time Object Detection. CoRR abs\/1506.02640(2015). http:\/\/arxiv.org\/abs\/1506.02640"},{"key":"e_1_3_2_1_13_1","unstructured":"Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. CoRR abs\/1612.08242(2016). http:\/\/arxiv.org\/abs\/1612.08242  Joseph Redmon and Ali Farhadi. 2016. YOLO9000: Better Faster Stronger. CoRR abs\/1612.08242(2016). http:\/\/arxiv.org\/abs\/1612.08242"},{"key":"e_1_3_2_1_14_1","unstructured":"Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767(2018). https:\/\/arxiv.org\/abs\/1804.02767  Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767(2018). https:\/\/arxiv.org\/abs\/1804.02767"},{"key":"e_1_3_2_1_15_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014).  Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014)."},{"key":"e_1_3_2_1_16_1","unstructured":"Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott\u00a0E. Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2014. Going Deeper with Convolutions. CoRR abs\/1409.4842(2014). http:\/\/arxiv.org\/abs\/1409.4842  Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott\u00a0E. Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2014. Going Deeper with Convolutions. CoRR abs\/1409.4842(2014). http:\/\/arxiv.org\/abs\/1409.4842"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym12030427"}],"event":{"name":"ICVIP 2022: 2022 The 6th International Conference on Video and Image Processing","acronym":"ICVIP 2022","location":"Shanghai China"},"container-title":["2022 The 6th International Conference on Video and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579109.3579110","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3579109.3579110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:05Z","timestamp":1750178285000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579109.3579110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":17,"alternative-id":["10.1145\/3579109.3579110","10.1145\/3579109"],"URL":"https:\/\/doi.org\/10.1145\/3579109.3579110","relation":{},"subject":[],"published":{"date-parts":[[2022,12,23]]},"assertion":[{"value":"2023-03-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}