{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:15:44Z","timestamp":1770142544991,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"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":[[2022,11,17]]},"DOI":"10.1145\/3581807.3581888","type":"proceedings-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:02:28Z","timestamp":1684800148000},"page":"551-558","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["LEPD-Net: A Lightweight Efficient Network with Pyramid Dilated Convolution for Seed Sorting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0218-7035","authenticated-orcid":false,"given":"Weijie","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Information, Zhong yuan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2617-8985","authenticated-orcid":false,"given":"Pingsun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhong yuan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5115-8201","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhong yuan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2382-1691","authenticated-orcid":false,"given":"Xiaoting","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhong yuan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-909X","authenticated-orcid":false,"given":"Xiaomin","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhong yuan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6106-0416","authenticated-orcid":false,"given":"Zhenghui","family":"Hu","sequence":"additional","affiliation":[{"name":"Hangzhou Innovation Institute, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Assembly Automation","author":"Shiqing Wu","year":"2020","unstructured":"Shiqing Wu , Zhonghou Wang, Bin Shen , Jia-Hai Wang, Li Dongdong , Human-computer interaction based on machine vision of a smart assembly workbench . Assembly Automation , 2020 , ahead-of-print(ahead-of-print). DOI: 10.1108\/aa-10-2018-0170 10.1108\/aa-10-2018-0170 Shiqing Wu, Zhonghou Wang, Bin Shen, Jia-Hai Wang, Li Dongdong, Human-computer interaction based on machine vision of a smart assembly workbench. Assembly Automation, 2020, ahead-of-print(ahead-of-print). DOI: 10.1108\/aa-10-2018-0170"},{"key":"e_1_3_2_1_2_1","first-page":"1","volume-title":"Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines,\"\u00a02018 26th Signal Processing and Communications Applications Conference (SIU)","author":"Altunta\u015f A. F.","year":"2018","unstructured":"Y. Altunta\u015f , A. F. Kocamaz , R. Cengiz and M. Esmeray , \" Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines,\"\u00a02018 26th Signal Processing and Communications Applications Conference (SIU) , 2018 , pp. 1 - 4 , doi: 10.1109\/SIU.2018.8404800. 10.1109\/SIU.2018.8404800 Y. Altunta\u015f, A. F. Kocamaz, R. Cengiz and M. Esmeray, \"Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines,\"\u00a02018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1-4, doi: 10.1109\/SIU.2018.8404800."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jspr.2014.10.001","article-title":"Discriminating and elimination of damaged soybean seeds based on image characteristics","volume":"60","author":"Liu D","year":"2014","unstructured":"Liu D , Ning X , Li Z , Discriminating and elimination of damaged soybean seeds based on image characteristics . Journal of Stored Products Research , 2014 , 60 : 67 - 74 . Liu D, Ning X, Li Z, Discriminating and elimination of damaged soybean seeds based on image characteristics. Journal of Stored Products Research, 2014, 60:67-74.","journal-title":"Journal of Stored Products Research"},{"key":"e_1_3_2_1_4_1","volume-title":"Corn classification system based on computer vision","author":"Li X","year":"2019","unstructured":"Li X , Dai B , Sun H , Corn classification system based on computer vision 2019 , 11(4), 591; https:\/\/doi.org\/10.3390\/sym11040591. 10.3390\/sym11040591 Li X, Dai B, Sun H, Corn classification system based on computer vision 2019, 11(4), 591; https:\/\/doi.org\/10.3390\/sym11040591."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","first-page":"105507","DOI":"10.1016\/j.compag.2020.105507","article-title":"Multiclass classification of dry beans using computer vision and machine learning techniques","volume":"174","author":"Koklu M","year":"2020","unstructured":"Koklu M , Ozkan I A . Multiclass classification of dry beans using computer vision and machine learning techniques . Computers and Electronics in Agriculture , 2020 , 174 : 105507 . Koklu M, Ozkan I A. Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture, 2020, 174: 105507.","journal-title":"Computers and Electronics in Agriculture"},{"key":"e_1_3_2_1_6_1","volume-title":"Attribute-Aware Attention Model for Fine-grained Representation Learning","author":"Kai Han","year":"2019","unstructured":"Kai Han and Jian yuan Guo and Chao Zhang and Mingjian Zhu , Attribute-Aware Attention Model for Fine-grained Representation Learning . 2019 , CoRR , http:\/\/arxiv.org\/abs\/1901.00392. Kai Han and Jian yuan Guo and Chao Zhang and Mingjian Zhu, Attribute-Aware Attention Model for Fine-grained Representation Learning. 2019, CoRR, http:\/\/arxiv.org\/abs\/1901.00392."},{"key":"e_1_3_2_1_7_1","first-page":"10","article-title":"Deep learning-based approach using X-ray images for classifying Crambe abyssinica seed quality","volume":"164","author":"Andr\u00e9 Dantas","year":"2021","unstructured":"Andr\u00e9 Dantas {de Medeiros} and Rodrigo Cupertino Bernardes and La\u00e9rcio Junio . Deep learning-based approach using X-ray images for classifying Crambe abyssinica seed quality . Industrial Crops and Products , 2021 , 164 , https:\/\/doi.org\/ 10 .1016\/j.indcrop.2021.113378. 10.1016\/j.indcrop.2021.113378 Andr\u00e9 Dantas {de Medeiros} and Rodrigo Cupertino Bernardes and La\u00e9rcio Junio. Deep learning-based approach using X-ray images for classifying Crambe abyssinica seed quality. Industrial Crops and Products, 2021, 164, https:\/\/doi.org\/10.1016\/j.indcrop.2021.113378.","journal-title":"Industrial Crops and Products"},{"key":"e_1_3_2_1_8_1","volume-title":"A Deep Convolutional Neural Network Architecture for Boosting Image Discrimination Accuracy of Rice Species.\u00a0Food Bioprocess Technol\u00a011, 765\u2013773","author":"Lin P.","year":"2018","unstructured":"Lin , P. , Li , X.L. , Chen , Y.M. A Deep Convolutional Neural Network Architecture for Boosting Image Discrimination Accuracy of Rice Species.\u00a0Food Bioprocess Technol\u00a011, 765\u2013773 ( 2018 ). https:\/\/doi.org\/10.1007\/s11947-017-2050-9 10.1007\/s11947-017-2050-9 Lin, P., Li, X.L., Chen, Y.M.et al. A Deep Convolutional Neural Network Architecture for Boosting Image Discrimination Accuracy of Rice Species.\u00a0Food Bioprocess Technol\u00a011, 765\u2013773 (2018). https:\/\/doi.org\/10.1007\/s11947-017-2050-9"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/2716975","volume":"2019","author":"Huang S","year":"2019","unstructured":"Huang S , Fan X , Sun L , Research on Classification Method of Maize Seed Defect Based on Machine Vision . Journal of Sensors 2019 : 1 - 9 dio:10.1155\/ 2019 \/2716975 Huang S, Fan X, Sun L, Research on Classification Method of Maize Seed Defect Based on Machine Vision. Journal of Sensors 2019:1-9 dio:10.1155\/2019\/2716975","journal-title":"Journal of Sensors"},{"key":"e_1_3_2_1_10_1","unstructured":"Simonyan K. and A. Zisserman . \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" Computer Science (2014).  Simonyan K. and A. Zisserman . \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" Computer Science (2014)."},{"key":"e_1_3_2_1_11_1","volume-title":"Going Deeper with Convolutions[J]","author":"Szegedy C","year":"2014","unstructured":"Szegedy C , Liu W , Jia Y , Going Deeper with Convolutions[J] . IEEE Computer Society , 2014 . Szegedy C, Liu W, Jia Y, Going Deeper with Convolutions[J]. IEEE Computer Society, 2014."},{"key":"e_1_3_2_1_12_1","volume-title":"Inverted Residuals and Linear Bottlenecks.\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sandler M.","year":"2018","unstructured":"Sandler , M. , \" MobileNet V2 : Inverted Residuals and Linear Bottlenecks.\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition ( CVPR) IEEE , 2018 . Sandler, M. , \"MobileNetV2: Inverted Residuals and Linear Bottlenecks.\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE, 2018."},{"key":"e_1_3_2_1_13_1","first-page":"6848","volume-title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,\"\u00a02018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhang X.","year":"2018","unstructured":"X. Zhang , X. Zhou , M. Lin and J. Sun , \" ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,\"\u00a02018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition , 2018 , pp. 6848 - 6856 , doi: 10.1109\/CVPR.2018.00716. 10.1109\/CVPR.2018.00716 X. Zhang, X. Zhou, M. Lin and J. Sun, \"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,\"\u00a02018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 6848-6856, doi: 10.1109\/CVPR.2018.00716."},{"issue":"2","key":"e_1_3_2_1_14_1","first-page":"287","article-title":"A CNN-based lightweight ensemble model for detecting defective carrots","volume":"208","author":"Weijun Xie","year":"2021","unstructured":"Weijun Xie and Shuo Wei and Zhaohui Zheng and Deyong Yang . A CNN-based lightweight ensemble model for detecting defective carrots . Biosystems Engineering , 2021 , 208 ( 2 ): 287 - 299 , https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.06.008. 10.1016\/j.biosystemseng.2021.06.008 Weijun Xie and Shuo Wei and Zhaohui Zheng and Deyong Yang. A CNN-based lightweight ensemble model for detecting defective carrots. Biosystems Engineering, 2021, 208(2):287-299, https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.06.008.","journal-title":"Biosystems Engineering"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Zhao G Quan L Li H Real-time recognition system of soybean seed full-surface defects based on deep learning ScienceDirect. Computers and Electronics in Agriculture 187 106230 doi: https:\/\/doi.org\/10.1016\/j.compag.2021.106230.    10.1016\/j.compag.2021.106230\nZhao G Quan L Li H Real-time recognition system of soybean seed full-surface defects based on deep learning ScienceDirect. Computers and Electronics in Agriculture 187 106230 doi: https:\/\/doi.org\/10.1016\/j.compag.2021.106230.","DOI":"10.1016\/j.compag.2021.106230"},{"key":"e_1_3_2_1_16_1","first-page":"13708","volume-title":"Coordinate Attention for Efficient Mobile Network Design,\"\u00a02021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Hou D.","year":"2021","unstructured":"Q. Hou , D. Zhou and J. Feng , \" Coordinate Attention for Efficient Mobile Network Design,\"\u00a02021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021 , pp. 13708 - 13717 , doi: 10.1109\/CVPR46437.2021.01350. 10.1109\/CVPR46437.2021.01350 Q. Hou, D. Zhou and J. Feng, \"Coordinate Attention for Efficient Mobile Network Design,\"\u00a02021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 13708-13717, doi: 10.1109\/CVPR46437.2021.01350."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"He\n      K Zhang\n      X Ren\n      S Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition.\n  2016\n  :  \n  770\n  -\n  778\n  .  He K Zhang X Ren S Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_18_1","volume-title":"DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation","author":"Li G","year":"2019","unstructured":"Li G , Yun I, J Kim , DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation . 2019 , CoRR , http:\/\/arxiv.org\/abs\/1907.11357. Li G, Yun I, J Kim, DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation. 2019, CoRR, http:\/\/arxiv.org\/abs\/1907.11357."},{"key":"e_1_3_2_1_19_1","volume-title":"PP-LCNet: A Lightweight CPU Convolutional Neural Network","author":"Cui C","year":"2021","unstructured":"Cui C , Gao T , Wei S , PP-LCNet: A Lightweight CPU Convolutional Neural Network . 2021 , CoRR , abs\/2109.15099, https:\/\/arxiv.org\/abs\/2109.15099. Cui C, Gao T, Wei S, PP-LCNet: A Lightweight CPU Convolutional Neural Network. 2021, CoRR, abs\/2109.15099, https:\/\/arxiv.org\/abs\/2109.15099."},{"key":"e_1_3_2_1_20_1","volume-title":"Searching for Activation Functions","author":"Ramachandran P","year":"2017","unstructured":"Ramachandran P , Zoph B , Le Q V . Searching for Activation Functions . 2017 , abs\/1710.05941. Ramachandran P, Zoph B, Le Q V. Searching for Activation Functions. 2017, abs\/1710.05941."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Hu J Shen L Sun G. Squeeze-and-excitation networks.\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.  Hu J Shen L Sun G. Squeeze-and-excitation networks.\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","first-page":"104874","DOI":"10.1016\/j.compag.2019.104874","article-title":"Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach","volume":"163","author":"Altunta\u015f Y","year":"2019","unstructured":"Altunta\u015f Y , C\u00f6mert Z , Kocamaz A F . Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach . Computers and Electronics in Agriculture , 2019 , 163 : 104874 . Altunta\u015f Y, C\u00f6mert Z, Kocamaz A F. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Computers and Electronics in Agriculture, 2019, 163: 104874.","journal-title":"Computers and Electronics in Agriculture"},{"key":"e_1_3_2_1_23_1","volume-title":"Karayev S","author":"Iandola F","year":"1869","unstructured":"Iandola F , Moskewicz M , Karayev S , Densenet : Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404. 1869 , 2014. Iandola F, Moskewicz M, Karayev S, Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869, 2014."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020:  1580-1589","author":"Han K","unstructured":"Han K , Wang Y , Tian Q , Ghostnet : More features from cheap operations . Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589 . Han K, Wang Y, Tian Q, Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589."},{"key":"e_1_3_2_1_25_1","volume-title":"Le Q V. Mixconv: Mixed depthwise convolutional kernels. arXiv preprint arXiv:1907.09595","author":"Tan M","year":"2019","unstructured":"Tan M , Le Q V. Mixconv: Mixed depthwise convolutional kernels. arXiv preprint arXiv:1907.09595 , 2019 . Tan M, Le Q V. Mixconv: Mixed depthwise convolutional kernels. arXiv preprint arXiv:1907.09595, 2019."},{"key":"e_1_3_2_1_26_1","volume-title":"PMLR","author":"Tan M","unstructured":"Tan M , Le Q. Efficientnet : Rethinking model scaling for convolutional neural networks. nternational conference on machine learning . PMLR , 2019: 6105-6114. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. nternational conference on machine learning. PMLR, 2019: 6105-6114."},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the IEEE international conference on computer vision. 2017:  618-626","author":"Selvaraju R R","unstructured":"Selvaraju R R , Cogswell M , Das A , Grad -cam : Visual explanations from deep networks via gradient-based localization . Proceedings of the IEEE international conference on computer vision. 2017: 618-626 . Selvaraju R R, Cogswell M, Das A, Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision. 2017: 618-626."}],"event":{"name":"ICCPR 2022: 2022 11th International Conference on Computing and Pattern Recognition","location":"Beijing China","acronym":"ICCPR 2022"},"container-title":["Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581807.3581888","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581807.3581888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:30Z","timestamp":1750182570000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581807.3581888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":27,"alternative-id":["10.1145\/3581807.3581888","10.1145\/3581807"],"URL":"https:\/\/doi.org\/10.1145\/3581807.3581888","relation":{},"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"2023-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}