Research on Target Detection Algorithm Based on Aerial Video
DOI:
https://doi.org/10.54097/84za8m76Keywords:
Deep learning; UAV aerial photography; YOLOv5; attention module; target detection algorithm.Abstract
For the Yolov5 target detection algorithm, firstly, the DPFEM network is proposed to replace the original BottleneckCSP and C3 network structure for the problem of inadequate feature extraction for small targets, secondly, the MAFFEM module is proposed to alleviate the conflict of feature fusion due to the fusion conflicts brought by the different scales of the feature maps during the feature fusion, finally, the training The results show that the improved Yolov5 algorithm mAP0.5 and mAP0.5:0.95 are improved by 2.2% and 1.7%, respectively, and have certain application potential.
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Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate objectdetection and semantic segmentation [C]//Proceedings of the IEEE conference on computervision and pattern recognition. 2014: 580-587.
Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computervision. 2015: 1440-1448.
Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with regionproposal networks[J].Advances in neural information processing systems, 2015, 28: 91-99.
X. Z. Wang,C. He. Vehicle detection in complex environments based on Transformer improved Faster RCNN[J/OL]. Electromechanical Engineering Technology. https://link.cnki.net/urlid/44.1522.TH.20240312.1557.002.
SHAO-HONG ZHOU, XIN-JIN FANG, XIN-YI LIU, EDDIE ZHANG, SHENG YAN. Aircraft target detection based on migration learning and improved Faster-RCNN remote sensing imagery[J/OL]. Mechatronics.https://link.cnki.net/urlid/44.1522.TH.20240222.1433.002.
Du Yunyan,Yang Jinhui,Li Hong,et al. Sample less target detection algorithm based on improved Faster RCNN[J]. Electro-Optics and Control,2023,30(5):44-51.
Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 779-788.
Rothe R, Guillaumin M, Van Gool L. Non-maximum suppression for object detection by passing messages between windows[C]//Asian Conference on Computer Vision. Springer, Cham, 2014: 290-306.
Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]. European conference on computer vision. Springer, Cham, 2016: 21-37.
Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 7263-7271.
Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.
Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C] //Proceedings of the IEEE International Conference on Computer Vision. 2017: 2980-2988.
Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017:2117-2125.
Y . Chen et al., “Dual path networks,” in Proc. Adv. Neural Inf. Process.Syst., 2017, pp. 1–9.
Wang Y, Xie L, Liu C, et al. Sort: Second-order response transform for visual recognition[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1359-1368.
ZHAO Lulu, WANG Xueying, ZHANG Yi, et al. Research on vehicle target detection technology based on YOLOv5s fusion SENet[J/OL]. Journal of Graphics:1-8[2022-05-23].http://kns.cnki.net/kcms/detail/10.1034.T.20220513.1447.004.html
Kuang Xianxiang,Liu Ping. Vehicle detection method for complex scenes based on improved YOLOv5s[J]. Modern Computer,2022,28(07):47-52.
Feng Z, Xie ZJ, Bao ZW, et al. Real-time dense small target detection algorithm for UAV based on improved YOLOv5[J/OL]. Journal of Aeronautics:1-15[2022-05-16].http://kns.cnki.net/kcms/detail/11.1929.V.20220509.2316.010.html
LONG Sai,SONG Xiaofeng,ZHANG Su,et al. Research on vehicle detection in aerial images with improved YOLOv5s[J/OL]. Laser Journal:1-9[2022-05-23].http://kns.cnki.net/kcms/detail/50.1085.TN.20220331.1716.026.html
LIU Chaoyang,QU Jinshuai,FAN Jing,et al. Vehicle target detection based on improved YOLOv5 algorithm[J/OL]. Journal of Yunnan University for Nationalities(Natural Science Edition):1-9[2022-05-23].http://kns.cnki.net/kcms/detail/53.1192.N.20220421.0940.015.html
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