SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images

References

• Bodla N, Singh B, Chellappa R, Davis LS. 2017. Soft-NMS–improving object detection with one line of code. Paper Presented at the Proceedings of the IEEE International Conference on Computer Vision.
   Google Scholar
• Bochkovskiy A, Wang C-Y, Liao H-YM. 2020. Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. doi: 10.48550/arXiv.2004.10934.
   Google Scholar
• Cai Z, Vasconcelos N. 2019. Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans Pattern Anal Mach Intell. 43(5):1483–1498. doi: 10.1109/TPAMI.2019.2956516.
   Web of Science ®Google Scholar
• Deng S, Li S, Xie K, Song W, Liao X, Hao A, Qin H. 2021. A global-local self-adaptive network for drone-view object detection. IEEE Trans Image Process. 30:1556–1569. doi: 10.1109/tip.2020.3045636.
   PubMed Web of Science ®Google Scholar
• Jocher G, Chaurasia A, Qiu J. “YOLO by Ultralytics.” Accessed. 2023. https://github. com/ultralytics/ultralytics., February 30, 2023.
   Google Scholar
• Jocher G. 2020. “YOLOv5 by Ultralytics.” https://github.com/ultralytics/yolov5., Accessed: February 30, 2023.
   Google Scholar
• Gao J, Chen Y, Wei Y, Li J. 2021. Detection of specific building in remote sensing images using a novel YOLO-S-CIOU model. Case: gas station identification. Sensors (Basel). 21(4):1375. doi: 10.3390/s21041375.
   PubMed Web of Science ®Google Scholar
• Ge Z, Liu S, Wang F, Li Z, Sun J. 2021. Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430. doi: 10.48550/arXiv.2107.08430.
   Google Scholar
• Girshick R. 2015. Fast r-cnn. Paper Presented at the Proceedings of the IEEE International Conference on Computer Vision.
   Google Scholar
• Girshick R, Donahue J, Darrell T, Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. doi: 10.1109/CVPR.2014.81.
   Google Scholar
• Guo D, Wang Y, Zhu S, Li X. 2023. A vehicle detection method based on an improved u-yolo network for high-resolution remote-sensing images. Sustainability. 15(13):10397. doi: 10.3390/su151310397.
   Web of Science ®Google Scholar
• Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. doi: 10.1109/CVPR.2018.00745.
   Google Scholar
• Kou X, Liu S, Cheng K, Qian Y. 2021. Development of a YOLO-V3-based model for detecting defects on steel strip surface. Measurement. 182:109454. doi: 10.1016/j.measurement.2021.109454.
   Web of Science ®Google Scholar
• Li C, Li L, Jiang H, Weng K, Geng Y, Li L, et al. 2022. YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976. doi: 10.48550/arXiv.2209.02976.
   Google Scholar
• Li C, Yang T, Zhu S, Chen C, Guan S. 2020. Density map guided object detection in aerial images. Paper presented at the proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. doi: 10.1109/CVPRW50498.2020.00103.
   Google Scholar
• Li Z, Yuan J, Li G, Wang H, Li X, Li D, Wang X. 2023. Rsi-yolo: object detection method for remote sensing images based on improved yolo. Sensors (Basel). 23(14):6414. doi: 10.3390/s23146414.
   PubMed Web of Science ®Google Scholar
• Li Z, Wang Y, Chen K, Yu Z. 2022. Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection. arXiv Preprint. arXiv:2204.13699. doi: 10.48550/arXiv.2204.13699.
   Google Scholar
• Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S. 2017. Feature pyramid networks for object detection. Paper Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR.2017.106.
   Google Scholar
• Lin T-Y, Goyal P, Girshick R, He K, Dollár P. 2017. Focal loss for dense object detection. Paper presented at the Proceedings of the IEEE international conference on computer vision. doi: 10.1109/ICCV.2017.324.
   Google Scholar
• Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC. 2016. Ssd: single shot multibox detector. Paper presented at the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. doi: 10.1007/978-3-319-46448-0_2.
   Google Scholar
• Liu Z, Gao Y, Du Q. 2023. Yolo-class: detection and classification of aircraft targets in satellite remote sensing images based on yolo-extract. IEEE Access. 11:109179–109188. doi: 10.1109/ACCESS.2023.3321828.
   Google Scholar
• Long X, Deng K, Wang G, Zhang Y, Dang Q, Gao Y, et al. 2020. PP-YOLO: an effective and efficient implementation of object detector. arXiv preprint arXiv:2007.12099. doi: 10.48550/arXiv.2007.12099.
   Google Scholar
• Luo X, Wu Y, Wang F. 2022. Target detection method of UAV aerial imagery based on improved YOLOv5. Remote Sensing. 14(19):5063. doi: 10.3390/rs14195063.
   Web of Science ®Google Scholar
• Ma Y, Yu D, Wu T, Wang H. 2019. Paddlepaddle: an open-source deep learning platform from industrial practice. Frontiers of Data and Domputing. 1(1):105–115. doi: 10.11871/jfdc.issn.2096.742X.2019.01.011.
   Google Scholar
• Pham M-T, Courtrai L, Friguet C, Lefèvre S, Baussard A. 2020. YOLO-Fine: one-stage detector of small objects under various backgrounds in remote sensing images. Remote Sensing. 12(15):2501. doi: 10.3390/rs12152501.
   Web of Science ®Google Scholar
• R. team. 2023. “YOLO-NAS by Deci Achieves State-of-the-Art Performance on Object Detection Using Neural Architecture Search.” https://deci.ai/blog/yolo-nas-object-detection-foundation-model/., Accessed: May 12, 2023.
   Google Scholar
• Redmon J, Divvala S, Girshick R, Farhadi A. 2016. You only look once: unified, real-time object detection. Paper Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR.2016.91.
   Google Scholar
• Redmon J, Farhadi A. 2017. YOLO9000: better, faster, stronger. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. doi: 10.1109/CVPR.2017.690.
   Google Scholar
• Redmon J, Farhadi A. 2018. Yolov3: an incremental improvement. arXiv Preprint. arXiv:1804.02767. doi: 10.48550/arXiv.1804.02767.
   Google Scholar
• Ren S, He K, Girshick R, Sun J. 2015. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 39(6):1137–1149. doi: 10.1109/TPAMI.2016.2577031.
   Web of Science ®Google Scholar
• Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S. 2019. Generalized intersection over union: a metric and a loss for bounding box regression. Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. doi: 10.1109/CVPR.2019.00075.
   Google Scholar
• Sunkara R, Luo T. 2022. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. Paper presented at the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. doi: 10.1007/978-3-031-26409-2_27.
   Google Scholar
• Suo J, Wang T, Zhang X, Chen H, Zhou W, Shi W. 2023. HIT-UAV: a high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection. Sci Data. 10(1):227. doi: 10.1038/s41597-023-02066-6.
   PubMed Web of Science ®Google Scholar
• Su Z, Yu J, Tan H, Wan X, Qi K. 2023. Msa-yolo: a remote sensing object detection model based on multi-scale strip attention. Sensors (Basel). 23(15):6811. doi: 10.3390/s23156811.
   PubMed Web of Science ®Google Scholar
• Tan M, Le Q. 2019. Efficientnet: rethinking model scaling for convolutional neural networks. Paper presented at the International conference on machine learning.
   Google Scholar
• Wang C, Bochkovskiy A, Liao H. 2022. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv 2022. arXiv preprint arXiv:2207.02696. doi: 10.1109/CVPR527292023.00721.
   Google Scholar
• Wang C-Y, Bochkovskiy A, Liao H-YM. 2021. Scaled-yolov4: scaling cross stage partial network. Paper presented at the Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. doi: 10.1109/CVPR46437.2021.01283.
   Google Scholar
• Wang J, Xu C, Yang W, Yu L. 2021. A normalized Gaussian Wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389. doi: 10.48550/arXiv.2110.13389.
   Google Scholar
• Wang W, Dai J, Chen Z, Huang Z, Li Z, Zhu X, Hu X, Lu T, Lu L, Li H. 2023. Internimage: exploring large-scale vision foundation models with deformable convolutionsed. Eds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14408–14419.
   Google Scholar
• Wang Y, Yang Y, Zhao X. 2020. Object detection using clustering algorithm adaptive searching regions in aerial images. Paper presented at the European Conference on Computer Vision. doi: 10.1007/978-3-030-66823-5_39.
   Google Scholar
• Wu T, Dong Y. 2023. Yolo-se: improved yolov8 for remote sensing object detection and recognition. Appl Sci. 13(24):12977. doi: 10.3390/app132412977.
   Google Scholar
• Xi Y, Jia W, Miao Q, Liu X, Fan X, Li H. 2022. FiFoNet: fine-grained target focusing network for object detection in UAV images. Remote Sens. 14(16):3919. doi: 10.3390/rs14163919.
   Google Scholar
• Xu X, Jiang Y, Chen W, Huang Y, Zhang Y, Sun X. 2022. Damo-yolo: a report on real-time object detection design. arXiv preprint arXiv:2211.15444. doi: 10.48550/arXiv.2211.15444.
   Google Scholar
• Xu D, Wu Y. 2021. Fe-yolo: a feature enhancement network for remote sensing target detection. Remote Sensing. 13(7):1311. doi: 10.3390/rs13071311.
   Web of Science ®Google Scholar
• Yang C, Huang Z, Wang N. 2022. QueryDet: cascaded sparse query for accelerating high-resolution small object detection. Paper Presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR52688.2022.01330.
   Google Scholar
• Yang F, Fan H, Chu P, Blasch E, Ling H. 2019. Clustered object detection in aerial images. Paper presented at the Proceedings of the IEEE/CVF international conference on computer vision. doi: 10.1109/ICCV.2019.00840.
   Google Scholar
• Yu W, Yang T, Chen C. 2021. Towards resolving the challenge of long-tail distribution in UAV images for object detection. Paper Presented at the Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. doi: 10.1109/WACV48630.2021.00330.
   Google Scholar
• Yu W, Zhou P, Yan S, Wang X. 2023. Inceptionnext: when inception meets convnext arXiv preprint arXiv:2303.16900. doi: 10.48550/arXiv.2303.16900.
   Google Scholar
• Yu Z, Huang H, Chen W, Su Y, Liu Y, Wang X. 2022. Yolo-facev2: a scale and occlusion aware face detector. arXiv preprint arXiv:2208.02019. doi: 10.48550/arXiv.2208.02019.
   Google Scholar
• Yu Z, Liu Y, Yu S, Wang R, Song Z, Yan Y, Li F, Wang Z, Tian F. 2022. Automatic detection method of dairy cow feeding behaviour based on YOLO improved model and edge computing. Sensors (Basel). 22(9):3271. doi: 10.3390/s22093271.
   PubMed Web of Science ®Google Scholar
• Zhang H, Hao C, Song W, Jiang B, Li B. 2023. Adaptive Slicing-Aided Hyper Inference for Small Object Detection in High-Resolution Remote Sensing Images. Remote Sensing. 15(5):1249. doi: 10.3390/rs15051249.
   Web of Science ®Google Scholar
• Zhu L, Wang X, Ke Z, Zhang W, Lau RW. 2023. BiFormer: vision transformer with bi-level routing attentioned. eds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p. 10323–10333.
   Google Scholar
• Zhu P, Wen L, Du D, Bian X, Fan H, Hu Q, Ling H. 2021. Detection and tracking meet drones challenge. IEEE Trans Pattern Anal Mach Intell. 44(11):7380–7399. doi: 10.1109/TPAMI.2021.3119563.
   Web of Science ®Google Scholar
• Zhu X, Lyu S, Wang X, Zhao Q. 2021. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. Paper presented at the Proceedings of the IEEE/CVF international conference on computer vision. doi: 10.1109/ICCVW54120.2021.00312.
   Google Scholar