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针对现存无人机航拍图像目标检测算法检测精度较低、模型较为复杂的问题，提出一种改进YOLOv8的目标检测算法。在骨干网络引入多尺度注意力EMA,捕捉细节信息，以提高模型的特征提取能力；改进C2f模块，减小模型的计算量。提出了轻量级的Bi-YOLOv8特征金字塔网络结构改进YOLOv8的颈部，增强了模型多尺度特征融合能力，改善网络对小目标的检测精度。使用WIoU Loss优化原网络损失函数，引入一种动态非单调聚焦机制，提高模型的泛化能力。在无人机航拍数据集VisDrone2019上的实验表明，提出算法的mAP50为40.7%,较YOLOv8s提升了1.5%,参数量降低了42%,同时相比于其他先进的目标检测算法在精度和速度上均有提升，证明了改进算法的有效性和先进性。
Abstract：To solve the problem that the existing UAV aerial image target detection algorithm has low detection accuracy and complex model, an improved YOLOv8 target detection algorithm is proposed. Multi-scale attention EMA is introduced into the backbone network to capture detailed information to improve the feature extraction ability and C2f module is improved to reduce the calculation amount of the model. The lightweight Bi-YOLOv8 feature pyramid network structure is proposed to improve the neck of YOLOv8, the multi-scale feature fusion ability of the model is enhanced, and the detection accuracy of the network for small targets is improved. WIoU Loss is used to optimize the original network loss function, and a dynamic non-monotonic focusing mechanism is introduced to improve the generalization ability of the model. Experiments on UAV aerial image data set VisDrone2019 show that the mAP50 of the proposed algorithm is 40.7%, which is 1.5% higher than YOLOv8s, and the number of parameters is reduced by 42%. The accuracy and speed are improved compared with other advanced target detection algorithms, which proves the effectiveness and advanced nature of the proposed algorithm.
[1] 柴兴华，胡炎，雷耀麟，等.无人机智能测控技术研究综述[J].无线电工程，2019,49(10):855-860.
[2] SURMANN H,WORST R,BUSCHMANN T,et al.Integration of UAVs in Urban Search and Rescue Missions[C]//2019 IEEE International Symposium on Safety,Security,and Rescue Robotics (SSRR).Würzburg:IEEE,2019:203-209.
[3] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot MultiBox Detector[C]//2016 European Conference on Computer Vision(ECCV).Amsterdam:Springer,2016:21-37.
[4] REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-time Object Detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas:IEEE,2016:779-788.
[5] 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(ICCV).Venice:IEEE,2017:2980-2988.
[6] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Columbus:IEEE,2014:580-587.
[7] CAI W W,WEI Z G.Remote Sensing Image Classification Based on a Cross-attention Mechanism and Graph Convolution[J].IEEE Geoscience and Remote Sensing Letters,2020,19:1-5.
[8] CHENG Q Q,WANG H J,DING X C,et al.A UAV Target Detection Algorithm Based on YOLOv4-tiny and Improved WBF[C]//Proceedings of the 14th International Conference on Wireless Communications and Signal Processing (WCSP).Nanjing:IEEE,2022:122-126.
[9] 罗旭鸿，刘永春，楚国铭，等.基于改进YOLOv5无人机图像目标检测算法[J].无线电工程，2023,53(7):1528-1535.
[10] LIU W J,QIANG J,LI X X,et al.UAV Image Small Object Detection Based on Composite Backbone Network[J].Mobile Information Systems,2022(15):1-11.
[11] WANG B B,YANG G J,YANG H,et al.Multiscale Maize Tassel Identification Based on Improved RetinaNet Model and UAV Images[J].Remote Sensing,2023,15(10):2530.
[12] 王殿伟，胡里晨，房杰，等.基于改进Double-Head RCNN的无人机航拍图像小目标检测算法[J].北京航空航天大学学报，2023:10.
[13] DEMESQUITA D B,SANTOS R F D,MACHARET D G,et al.Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Images[J].IEEE Geoscience and Remote Sensing Letters,2020,17(8):1455-1459.
[14] 赵耘彻，张文胜，刘世伟.基于改进YOLOv4的无人机航拍目标检测算法[J].电子测量技术，2023,46(8):169-175.
[15] WANG X,HE N,HONG C,et al.Improved YOLOX-X Based UAV Aerial Photography Object Detection Algorithm[J].Image and Vision Computing,2023,135:104697.
[16] WANG C Y,BOCHKOVSKIY A,LIAO H Y M.YOLOv7:Trainable Bag-of-freebies Sets New State-of-the-art for Real-time Object Detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Vancouver:IEEE,2023:7464-7475.
[17] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu:IEEE,2017:936-944.
[18] LIUS,QI L,QIN H F,et al.Path Aggregation Network for Instance Segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Salt Lake City:IEEE,2018:8759-8768.
[19] OUYANG D L,HE S,ZHANG G Z,et al.Efficient Multi-scale Attention Module with Cross-spatial Learning[C]//2023 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP).Rhodes Island:IEEE,2023:1-5.
[20] CHEN J R,KAO S H,HE H,et al.Run,Don’t Walk:Chasing Higher FLOPS for Faster Neural Networks[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Vancouver:IEEE,2023:12021-12031.
[21] TAN M X,PANG R M,LE G V.EfficientDet:Scalable and Efficient Object Detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle:IEEE,2020:10778-10787.
[22] ZHENG Z H,WANG P,REN D W,et al.Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation[J].IEEE Transactions on Cybernetics,2022,52(8):8574-8586.
[23] TONG Z J,CHEN Y H,XV Z W,et al.Wise-IoU:Bounding Box Regression Loss with Dynamic Focusing Mechanism[EB/OL].(2023-01-24)[2023-07-15].https://arxiv.org/abs/2301.10051.
[24] DU D W,ZHU P F,WEN L Y,et al.VisDrone-DET2019:The Vision Meets Drone Object Detection in Image Challenge Results[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).Seoul:IEEE,2019:213-226.
[25] DING J,XUE N,XIA G S J,et al.Object Detection in Aerial Images:A Large-scale Benchmark and Challenges[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44 (11):7778-7796.
基本信息:
DOI：
中图分类号:V279;TP391.41
引用信息:
[1]程换新,乔庆元,骆晓玲等.基于改进YOLOv8的无人机航拍图像目标检测算法[J].无线电工程,2024,54(04):871-881.
基金信息:
国家自然科学基金(62273192)~~