{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:09:36Z","timestamp":1776265776283,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031258244","type":"print"},{"value":"9783031258251","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25825-1_4","type":"book-chapter","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T19:02:52Z","timestamp":1675450972000},"page":"48-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Real-Time Kiwifruit Detection Based on Improved YOLOv7"],"prefix":"10.1007","author":[{"given":"Yi","family":"Xia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei Qi","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3441656","volume":"17","author":"N An","year":"2021","unstructured":"An, N., Yan, W.: Multitarget tracking using Siamese neural networks. ACM Trans. Multimed. Comput. Commun. App. 17, 1\u20136 (2021)","journal-title":"ACM Trans. Multimed. Comput. Commun. App."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"106066","DOI":"10.1016\/j.compag.2021.106066","volume":"183","author":"H Bazame","year":"2021","unstructured":"Bazame, H., Molin, J., Althoff, D., Martello, M.: Detection, classification, and mapping of coffee fruits during harvest with computer vision. Comput. Electron. Agric. 183, 106066 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"4_CR3","unstructured":"Bochkovskiy, A., Wang, C., Liao, H.: YOLOv4: Optimal speed and accuracy of object detection, https:\/\/arxiv.org\/abs\/2004.10934"},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision \u2013 ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/01140671.2004.9514276","volume":"32","author":"A Ferguson","year":"2004","unstructured":"Ferguson, A.: 1904\u2014the year that Kiwifruit (Actinidia deliciosa) came to New Zealand. N. Z. J. Crop. Hortic. Sci. 32, 3\u201327 (2004)","journal-title":"N. Z. J. Crop. Hortic. Sci."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/s42979-022-01152-7","volume":"3","author":"Y Fu","year":"2022","unstructured":"Fu, Y., Nguyen, M., Yan, W.Q.: Grading methods for fruit freshness based on deep learning. SN Comput. Sci. 3, 264 (2022)","journal-title":"SN Comput. Sci."},{"key":"4_CR7","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: Exceeding YOLO series in 2021 (2021). https:\/\/arxiv.org\/abs\/2107.08430"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"4_CR9","first-page":"498","volume":"5","author":"A Gongal","year":"2018","unstructured":"Gongal, A., Karkee, M., Amatya, S.: Apple fruit size estimation using a 3D machine vision system. Inf. Process. Agric. 5, 498\u2013503 (2018)","journal-title":"Inf. Process. Agric."},{"key":"4_CR10","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Jilbert, M. N., Jennifer, C.D.: On-tree mature coconut fruit detection based on deep learning using UAV images. In: IEEE International Conference on Cybernetics and Computational Intelligence, pp. 494\u2013499 (2022)","DOI":"10.1109\/CyberneticsCom55287.2022.9865266"},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"15221","DOI":"10.1109\/ACCESS.2021.3053167","volume":"9","author":"O Lawal","year":"2021","unstructured":"Lawal, O.: YOLOMuskmelon: quest for fruit detection speed and accuracy using deep learning. IEEE Access 9, 15221\u201315227 (2021)","journal-title":"IEEE Access"},{"key":"4_CR14","doi-asserted-by":"publisher","first-page":"942875","DOI":"10.3389\/fpls.2022.942875","volume":"13","author":"G Liu","year":"2022","unstructured":"Liu, G., Hou, Z., Liu, H., Liu, J., Zhao, W., Li, K.: TomatoDet: anchor-free detector for tomato detection. Front. Plant Sci. 13, 942875 (2022)","journal-title":"Front. Plant Sci."},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"6715","DOI":"10.3233\/JIFS-210597","volume":"41","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Yang, G., Huang, Y., Yin, Y.: SE-Mask R-CNN: an improved Mask R-CNN for apple detection and segmentation. J. Intell. Fuzzy Syst. 41, 6715\u20136725 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yan, W., Yang, B.: Image denoising based on a CNN model. In: IEEE ICCAR (2018)","DOI":"10.1109\/ICCAR.2018.8384706"},{"key":"4_CR17","unstructured":"Long, X., et al.: PP-YOLO: An effective and efficient implementation of object detector. https:\/\/arxiv.org\/abs\/2007.12099"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"106132","DOI":"10.1016\/j.compag.2021.106132","volume":"185","author":"J Massah","year":"2021","unstructured":"Massah, J., AsefpourVakilian, K., Shabanian, M., Shariatmadari, S.: Design, development, and performance evaluation of a robot for yield estimation of Kiwifruit. Comput. Electron. Agric. 185, 106132 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"e12335","DOI":"10.1111\/jfpe.12335","volume":"40","author":"E Olaniyi","year":"2016","unstructured":"Olaniyi, E., Oyedotun, O., Adnan, K.: Intelligent grading system for banana fruit using neural network arbitration. J. Food Process Eng. 40, e12335 (2016)","journal-title":"J. Food Process Eng."},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1109\/TIP.2021.3074796","volume":"30","author":"C Pan","year":"2021","unstructured":"Pan, C., Liu, J., Yan, W., et al.: Salient object detection based on visual perceptual saturation and two-stream hybrid networks. IEEE Trans. Image Process. 30, 4773\u20134787 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Pan, C., Yan, W.: A learning-based positive feedback in salient object detection. In: IEEE IVCNZ (2018)","DOI":"10.1109\/IVCNZ.2018.8634717"},{"issue":"27-28","key":"4_CR22","doi-asserted-by":"publisher","first-page":"19925","DOI":"10.1007\/s11042-020-08866-x","volume":"79","author":"C Pan","year":"2020","unstructured":"Pan, C., Yan, W.Q.: Object detection based on saturation of visual perception. Multimed. Tools App. 79(27\u201328), 19925\u201319944 (2020). https:\/\/doi.org\/10.1007\/s11042-020-08866-x","journal-title":"Multimed. Tools App."},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE CVPR, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"4_CR24","doi-asserted-by":"publisher","first-page":"160926","DOI":"10.1109\/ACCESS.2021.3132293","volume":"9","author":"T Shan","year":"2021","unstructured":"Shan, T., Yan, J.: SCA-Net: a spatial and channel attention network for medical image segmentation. IEEE Access. 9, 160926\u2013160937 (2021)","journal-title":"IEEE Access."},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Shen, D., Xin, C., Nguyen, M., Yan, W.: Flame detection using deep learning. In: IEEE ICCAR (2018)","DOI":"10.1109\/ICCAR.2018.8384711"},{"key":"4_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"4_CR27","unstructured":"Wang, C., Bochkovskiy, A., Liao, H.: Scaled-YOLOv4: Scaling cross stage partial network. https:\/\/arxiv.org\/abs\/2011.08036"},{"key":"4_CR28","unstructured":"Wang, C., Bochkovskiy, A., Liao, H.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https:\/\/arxiv.org\/abs\/2207.02696"},{"key":"4_CR29","unstructured":"Wang, C., Yeh, I., Liao, H.: You Only Learn One Representation: Unified network for multiple tasks. https:\/\/arxiv.org\/abs\/2105.04206"},{"key":"4_CR30","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-030-72073-5_3","volume-title":"Geometry and Vision","author":"L Wang","year":"2021","unstructured":"Wang, L., Yan, W.Q.: Tree leaves detection based on deep learning. In: Nguyen, M., Yan, W.Q., Ho, H. (eds.) Geometry and Vision. CCIS, vol. 1386, pp. 26\u201338. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72073-5_3"},{"key":"4_CR31","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: Efficient channel attention for deep convolutional neural networks. https:\/\/arxiv.org\/abs\/1910.03151"},{"key":"4_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"4_CR33","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-72073-5_5","volume-title":"Geometry and Vision","author":"B Xiao","year":"2021","unstructured":"Xiao, B., Nguyen, M., Yan, W.Q.: Apple ripeness identification using deep learning. In: Nguyen, M., Yan, W.Q., Ho, H. (eds.) Geometry and Vision. CCIS, vol. 1386, pp. 53\u201367. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72073-5_5"},{"key":"4_CR34","doi-asserted-by":"publisher","unstructured":"Yan, W.:Computational Methods for Deep Learning: Theoretic, Practice and Applications Texts in Computer Science. TCS. Springer, Cham (2021).https:\/\/doi.org\/10.1007\/978-3-030-61081-4","DOI":"10.1007\/978-3-030-61081-4"},{"key":"4_CR35","doi-asserted-by":"publisher","unstructured":"Yan, W.: Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics. 2nd Edn. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-60228-8","DOI":"10.1007\/978-3-319-60228-8"},{"key":"4_CR36","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-030-72073-5_24","volume-title":"Geometry and Vision","author":"K Zhao","year":"2021","unstructured":"Zhao, K., Yan, W.Q.: Fruit detection from digital images using CenterNet. In: Nguyen, M., Yan, W.Q., Ho, H. (eds.) Geometry and Vision. CCIS, vol. 1386, pp. 313\u2013326. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72073-5_24"},{"key":"4_CR37","first-page":"223","volume":"25","author":"K Zheng","year":"2017","unstructured":"Zheng, K., Yan, W., Nand, P.: Video dynamics detection using deep neural networks. IEEE Trans. Emerg. Top. Comput. Intell. 25, 223\u2013234 (2017)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cheng, D., Zhang, Z., Lin, S., Dai, J.: An empirical study of spatial attention mechanisms in deep networks. IEEE CVPR, pp. 6688\u20136697 (2019)","DOI":"10.1109\/ICCV.2019.00679"}],"container-title":["Lecture Notes in Computer Science","Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25825-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T15:59:06Z","timestamp":1728835146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25825-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258244","9783031258251"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25825-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IVCNZ","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Vision Computing New Zealand","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"37","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ivcnz2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ivcnz2022.aut.ac.nz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}