{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:43:18Z","timestamp":1781109798445,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013318","name":"General project of the Key R &amp; D Plan of Shanxi Province","doi-asserted-by":"publisher","award":["201903D121171"],"award-info":[{"award-number":["201903D121171"]}],"id":[{"id":"10.13039\/501100013318","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013318","name":"General project of the Key R &amp; D Plan of Shanxi Province","doi-asserted-by":"publisher","award":["61976134"],"award-info":[{"award-number":["61976134"]}],"id":[{"id":"10.13039\/501100013318","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["201903D121171"],"award-info":[{"award-number":["201903D121171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976134"],"award-info":[{"award-number":["61976134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model\u2019s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model\u2019s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.<\/jats:p>","DOI":"10.3390\/a16110520","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T09:46:13Z","timestamp":1699955173000},"page":"520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2632-6717","authenticated-orcid":false,"given":"Linhua","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Innovation, Design and Engineering, Malardalen University, 72123 Vasteras, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinghao","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-1345","authenticated-orcid":false,"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Institute of Shanghai University, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4784-8984","authenticated-orcid":false,"given":"Peng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiping","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38297","DOI":"10.1007\/s11042-022-13153-y","article-title":"Tools, techniques, datasets and application areas for object detection in an image: A review","volume":"81","author":"Kaur","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.ins.2016.07.042","article-title":"A Novel Spatio-Temporal Saliency Approach for Robust Dim Moving Target Detection from Airborne Infrared Image Sequences","volume":"369","author":"Li","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Hashmi, K.A., Pagani, A., Liwicki, M., Stricker, D., and Afzal, M.Z. (2021). Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments. Sensors, 21.","DOI":"10.20944\/preprints202106.0590.v1"},{"key":"ref_5","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 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_7","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, W., Huang, H., Li, D., Chen, F., and Cheng, W. (2020). Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN. Sensors, 20.","DOI":"10.3390\/s20174939"},{"key":"ref_9","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_11","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_12","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_14","unstructured":"Ultralytics (2021, November 01). Yolov5. [EB\/OL]. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11554-023-01268-w","article-title":"Real-Time Detection Algorithm of Helmet and Reflective Vest Based on Improved YOLOv5","volume":"20","author":"Chen","year":"2023","journal-title":"J. Real-Time Image Process"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, D., Jiang, S., Zhao, E., Liu, Y., Zhu, H., Wang, W., and Wang, R. (2022). Detection of Camellia oleifera Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation. Appl. Sci., 12.","DOI":"10.3390\/app122211318"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, T., Yan, R., Wen, X., Li, D., Jiang, H., Jiang, N., Feng, L., Duan, X., and Wang, J. (2022). An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation. Agriculture, 12.","DOI":"10.3390\/agriculture12101659"},{"key":"ref_18","unstructured":"Li, B., Chen, Y., Xu, H., and Fei, Z. (2023). Fast Vehicle Detection Algorithm on Lightweight YOLOv7-Tiny. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kulyukin, V.A., and Kulyukin, A.V. (2023). Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos from On-Hive Video Loggers with YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny. Sensors, 23.","DOI":"10.3390\/s23156791"},{"key":"ref_20","first-page":"21","article-title":"SSD: Single Shot Multibox Detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016, Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 11\u201314 October 2016"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050531"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fan, K., Hou, H., and Liu, C. (2022). Real-Time Detection of Drones Using Channel and Layer Pruning, Based on the YOLOv3-SPP3 Deep Learning Algorithm. Micromachines, 13.","DOI":"10.3390\/mi13122199"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, L., Liao, J., and Xu, C. (2019, January 22\u201324). Vehicle Detection Based on Drone Images with the Improved Faster R-CNN. Proceedings of the 2019 11th International Conference on Machine Learning and Computing (ICMLC\u201919), Zhuhai, China.","DOI":"10.1145\/3318299.3318383"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, H., Li, L., and Ma, H. (2022, January 26\u201328). An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images. Proceedings of the 2022 7th International Conference on Image, Vision and Computing (ICIVC), Xi\u2019an, China.","DOI":"10.1109\/ICIVC55077.2022.9886321"},{"key":"ref_25","unstructured":"Du, D., Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., Zheng, J., Peng, T., Wang, X., and Zhang, Y. (2019, January 27\u201328). VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, C., Xie, N., Yang, X., Chen, R., Chang, X., Zhong, R.Y., Peng, S., and Liu, X. (2022). A Domestic Trash Detection Model Based on Improved YOLOX. Sensors, 22.","DOI":"10.3390\/s22186974"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. (2023, January 18\u201322). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_28","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","article-title":"Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation","volume":"52","author":"Zheng","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","unstructured":"Huang, X., Wang, X., Lv, W., Bai, X., Long, X., Deng, K., Dang, Q., Han, S., Liu, Q., and Hu, X. (2021). PP-YOLOv2: A Practical Object Detector. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., and Liu, W. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). RepVGG: Making VGG-style ConvNets Great Again. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-Means Clustering Algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\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, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_39","first-page":"12993","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"34","author":"Zheng","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and Efficient IOU Loss for Accurate Bounding Box Regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_41","unstructured":"Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv."},{"key":"ref_42","first-page":"8577","article-title":"Gradient Harmonized Single-Stage Detector","volume":"33","author":"Li","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/11\/520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:22:57Z","timestamp":1760131377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/11\/520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"references-count":42,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["a16110520"],"URL":"https:\/\/doi.org\/10.3390\/a16110520","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,14]]}}}