{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:15:15Z","timestamp":1780485315528,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2016YFC0800901"],"award-info":[{"award-number":["2016YFC0800901"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid detection of illicit opium poppy plants using UAV (unmanned aerial vehicle) imagery has become an important means to prevent and combat crimes related to drug cultivation. However, current methods rely on time-consuming visual image interpretation. Here, the You Only Look Once version 3 (YOLOv3) network structure was used to assess the influence that different backbone networks have on the average precision and detection speed of an UAV-derived dataset of poppy imagery, with MobileNetv2 (MN) selected as the most suitable backbone network. A Spatial Pyramid Pooling (SPP) unit was introduced and Generalized Intersection over Union (GIoU) was used to calculate the coordinate loss. The resulting SPP-GIoU-YOLOv3-MN model improved the average precision by 1.62% (from 94.75% to 96.37%) without decreasing speed and achieved an average precision of 96.37%, with a detection speed of 29 FPS using an RTX 2080Ti platform. The sliding window method was used for detection in complete UAV images, which took approximately 2.2 sec\/image, approximately 10\u00d7 faster than visual interpretation. The proposed technique significantly improved the efficiency of poppy detection in UAV images while also maintaining a high detection accuracy. The proposed method is thus suitable for the rapid detection of illicit opium poppy cultivation in residential areas and farmland where UAVs with ordinary visible light cameras can be operated at low altitudes (relative height &lt; 200 m).<\/jats:p>","DOI":"10.3390\/s19224851","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:17:25Z","timestamp":1573125445000},"page":"4851","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Improved UAV Opium Poppy Detection Using an Updated YOLOv3 Model"],"prefix":"10.3390","volume":"19","author":[{"given":"Jun","family":"Zhou","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Tian","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Yin","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiping","family":"Wen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/01431161003713028","article-title":"Survey and monitoring of opium poppy and wheat in Afghanistan: 2003\u20132009","volume":"1","author":"Taylor","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Tian, Y.C., Yuan, C., Zhang, F.F., and Yang, G. (2018). Opium Poppy Detection Using Deep Learning. Remote Sens., 10.","DOI":"10.3390\/rs10121886"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1109\/TGRS.2011.2126582","article-title":"Spectral Discrimination of Opium Poppy Using Field Spectrometry","volume":"49","author":"Jia","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the International Conference on the Neural Information Processing Systems Conference, Lake Tahoe, NV, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_6","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"9905","author":"Liu","year":"2016","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Law, H., and Jia, D. (2018). CornerNet: Detecting Objects as Paired Keypoints. arXiv.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2019). CenterNet: Keypoint Triplets for Object Detection. arXiv.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","unstructured":"Kendall, A., Badrinarayanan, V., and Cipolla, R. (2015). Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the ICCV 2017 Best Paper Award: Mask R-CNN, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_19","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., and Vanhoucke, V. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_23","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_24","unstructured":"Howard, A.G., Zhu, M., Bo, C., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. arXiv.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Jian, S. (2017). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Jian, S. (2018). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. arXiv.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_30","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv."},{"key":"ref_31","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 Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_32","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shen, Z.Q., Liu, Z., Li, J.G., Jiang, Y.G., Chen, Y.R., and Xue, X.Y. (2017, January 22\u201329). DSOD: Learning Deeply Supervised Object Detectors from Scratch. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.212"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., and Zuair, M. (2017). Deep Learning Approach for Car Detection in UAV Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040312"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/TGRS.2018.2790926","article-title":"Convolutional SVM Networks for Object Detection in UAV Imagery","volume":"56","author":"Bazi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, F., Ren, R.L., Van de Voorde, T., Xu, W.B., Zhou, G.Y., and Zhou, Y. (2018). Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sens., 10.","DOI":"10.3390\/rs10030443"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., Dobbs, D., Yari, M., and Starek, M.J. (2019). DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11091128"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence. Remote Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_39","unstructured":"Tzutalin (2017, May 03). LabelImg. Git Code. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_40","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2018). mixup: Beyond Empirical Risk Minimization. arXiv."},{"key":"ref_41","unstructured":"Zhang, Z., He, T., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019). Bag of Freebies for Training Object Detection Neural Networks. arXiv."},{"key":"ref_42","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway Networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V.D.M., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_44","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"8691","author":"He","year":"2014","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_46","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 Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4851\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:39Z","timestamp":1760189559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":46,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224851"],"URL":"https:\/\/doi.org\/10.3390\/s19224851","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,7]]}}}