{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:34:30Z","timestamp":1764174870966,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:00:00Z","timestamp":1596844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2572017CB34"],"award-info":[{"award-number":["2572017CB34"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a certain number of detection accuracy for image processing of fruits in trees using deep-learning methods, but the overall performance of these methods has not been satisfactory, and they have never been used in the detection of pine cones. In this paper, a pine cone detection method based on Boundary Equilibrium Generative Adversarial Networks (BEGAN) and You Only Look Once (YOLO) v3 mode is proposed to solve the problems of insufficient data set, inaccurate detection result and slow detection speed. First, we use traditional image augmentation technology and generative adversarial network BEGAN to implement data augmentation. Second, we introduced a densely connected network (DenseNet) structure in the backbone network of YOLOv3. Third, we expanded the detection scale of YOLOv3, and optimized the loss function of YOLOv3 using the Distance-IoU (DIoU) algorithm. Finally, we conducted a comparative experiment. The experimental results show that the performance of the model can be effectively improved by using BEGAN for data augmentation. Under same conditions, the improved YOLOv3 model is better than the Single Shot MultiBox Detector (SSD), the faster-regions with convolutional neural network (Faster R-CNN) and the original YOLOv3 model. The detection accuracy reaches 95.3%, and the detection efficiency is 37.8% higher than that of the original YOLOv3.<\/jats:p>","DOI":"10.3390\/s20164430","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T05:07:23Z","timestamp":1597036043000},"page":"4430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Ze","family":"Luo","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin 150040, China"},{"name":"School of Electrical Information Engineering, Hunan Institute of Technology, NO.18 Henghua Road, Hengyang 421010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiling","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1108\/IR-05-2016-0142","article-title":"Robots poised to revolutionise agriculture","volume":"43","author":"Bogue","year":"2016","journal-title":"Ind. Robot Int. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.biosystemseng.2018.04.009","article-title":"Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis","volume":"171","author":"Lu","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.compind.2018.03.007","article-title":"Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model","volume":"99","author":"Liu","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ifacol.2018.08.183","article-title":"Mature tomato fruit detection algorithm based on improved HSV and watershed algorithm","volume":"51","author":"Malik","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-019-09654-w","article-title":"Color-, depth-, and shape-based 3D fruit detection","volume":"21","author":"Lin","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5573\/IEIESPC.2015.4.1.035","article-title":"Deep convolution neural networks in computer vision: A review","volume":"4","author":"Yoo","year":"2015","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_7","first-page":"1","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., and Oprea, S. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","key":"ref_8","DOI":"10.1016\/j.asoc.2018.05.018"},{"doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2017). Improving deep learning using generic data augmentation. arXiv.","key":"ref_9","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1007\/s11042-017-5243-3","article-title":"Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation","volume":"78","author":"Zhang","year":"2019","journal-title":"Multimed. Tools. Appl."},{"unstructured":"Goodfellow, I.J., Pouget-Abadie, J., and Mirza, M. (2014, January 8\u201313). Generative adversarial networks. Proceedings of the 27th International Conference of Neural Information Processing Systems, Montreal, QC, Canada.","key":"ref_11"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.3389\/fpls.2019.01516","article-title":"Convolutional neural net-based cassava storage root counting using real and synthetic images","volume":"10","author":"Atanbori","year":"2019","journal-title":"Front. Plant Sci."},{"doi-asserted-by":"crossref","unstructured":"Chou, Y.C., Kuo, C.J., and Chen, T.T. (2019). Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry. Appl. Sci., 9.","key":"ref_13","DOI":"10.3390\/app9194166"},{"unstructured":"Berthelot, D., Schumm, T., and Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks. arXiv.","key":"ref_14"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.patrec.2018.04.028","article-title":"High-quality face image generated with conditional boundary equilibrium generative adversarial networks","volume":"111","author":"Huang","year":"2018","journal-title":"Pattern Recognit. Lett."},{"doi-asserted-by":"crossref","unstructured":"Shao, W.Z., Xu, J.J., and Chen, L. (2019, January 12\u201314). Tiny face hallucination via boundary equilibrium generative adversarial networks. Proceedings of the Tenth International Conference on Graphics and Image Processing (ICGIP 2018), Chengdu, China.","key":"ref_16","DOI":"10.1117\/12.2524361"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2013Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, Y., Gong, C., Chen, Y., and Yu, H. (2020). Applications of deep learning for dense scenes analysis in agriculture: A review. Sensors, 20.","key":"ref_18","DOI":"10.3390\/s20051520"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_19","DOI":"10.1109\/CVPR.2016.91"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_20","DOI":"10.1109\/CVPR.2017.690"},{"unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.","key":"ref_21"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Van Der Maaten, L. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","key":"ref_22","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., and Girshick, R. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","key":"ref_23","DOI":"10.1109\/CVPR.2017.106"},{"doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","key":"ref_24","DOI":"10.1109\/CVPR.2019.00075"},{"doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., and Liu, W. (2019). Distance-IoU loss: Faster and better learning for bounding box regression. arXiv.","key":"ref_25","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_26","first-page":"180350","article-title":"Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3","volume":"45","author":"Weicong","year":"2018","journal-title":"Opto-Electron. Eng."},{"unstructured":"Paszke, A., Gross, S., and Massa, F. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems 32 (NIPS~2019), Vancouver, BC, Canada.","key":"ref_27"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4430\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:58:09Z","timestamp":1760176689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,8]]},"references-count":27,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164430"],"URL":"https:\/\/doi.org\/10.3390\/s20164430","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,8,8]]}}}