基于改进YOLOv8s的军用飞机目标检测算法
Military Aircraft Target Detection Algorithm Based on Improved YOLOv8s
摘要: 基于YOLOv8s目标检测算法，提出了一种改进的军用飞机遥感图像目标检测算法。首先，引入Mixup数据增强方法；其次，修改网络结构，减少主干网络最后一个输出特征图的通道数为256；然后，在主干网络部分融合改进的SimAM注意力机制A；最后，使用改进的NWD损失作为位置损失函数。改进的算法在MAR20和NWPU VHR-10数据集上的mAP50分别比YOLOv8s提高了4.3%和2.2%，验证了改进算法的有效性。
Abstract: Based on the YOLOv8s object detection algorithm, an improved algorithm for object detection in military aircraft remote sensing images is proposed. Firstly, the Mixup data augmentation method is introduced; secondly, the network structure is modified to reduce the number of channels in the last output feature map of the backbone network to 256; then, an improved SimAM attention mechanism is integrated into the backbone network; finally, an improved NWD loss is used as the position loss function. The improved algorithm has increased the mAP50 on the MAR20 and NWPU VHR-10 datasets by 4.3% and 2.2% respectively compared to YOLOv8s, verifying the effectiveness of the improved algorithm.
文章引用：王广川, 赵寿为. 基于改进YOLOv8s的军用飞机目标检测算法[J]. 运筹与模糊学, 2024, 14(4): 8-16. https://doi.org/10.12677/orf.2024.144371

参考文献

[1] 王杰, 张上, 张岳, 等. 改进YOLOv5的军事飞机检测算法[J]. 无线电工程, 2024, 54(3): 589-596.
[2] Xu, S., Chen, Z., Zhang, H., Xue, L. and Su, H. (2024) Improved Remote Sensing Image Target Detection Based on Yolov7. Optoelectronics Letters, 20, 234-242. [Google Scholar] [CrossRef] 
[3] Cui, C., Wang, R., Wang, Y., Zhou, F., Bian, X. and Chen, J. (2024) Research on Optical Remote Sensing Image Target Detection Technique Based on Dch-Yolov7 Algorithm. IEEE Access, 12, 34741-34751. [Google Scholar] [CrossRef] 
[4] Sunkur, R., Kantamaneni, K., Bokhoree, C., Rathnayake, U. and Fernando, M. (2024) Mangrove Mapping and Monitoring Using Remote Sensing Techniques Towards Climate Change Resilience. Scientific Reports, 14, Article No. 6949. [Google Scholar] [CrossRef] [PubMed]
[5] 章程军, 胡晓兵, 魏上云, 等. 基于深度学习的遥感目标检测技术[J]. 计算机工程, 2024, 45(2): 594-600.
[6] Tan, M., Pang, R. and Le, Q.V. (2020) Efficient Det: Scalable and Efficient Object Detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 10778-10787. [Google Scholar] [CrossRef] 
[7] 谢俊章, 彭辉, 唐健峰, 等. 改进YOLOv4的密集遥感目标检测[J]. 计算机工程与应用, 2021, 57(22): 247-256.
[8] Zhang, H., Cisse, M., Dauphin, Y.N., et al. (2018) Mixup: Beyond Empirical Risk Minimization. International Conference on Learning Representations, Vancouver, 30 April-3 May 2018, 1-13.
[9] Xu, C., Wang, J., Yang, W., Yu, H., Yu, L. and Xia, G. (2022) Detecting Tiny Objects in Aerial Images: A Normalized Wasserstein Distance and a New Benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 190, 79-93. [Google Scholar] [CrossRef] 
[10] Yang, L., Zhang, R.-Y., Li, L. and Xie, X. (2021) SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, New York, 18-24 July 2021, 11863-11874.
[11] 禹文奇, 程塨, 王美君, 等. MAR20: 遥感图像军用飞机目标识别数据集[J]. 遥感学报, 2023, 27(12): 2688-2696.
[12] Li, X., Lv, C., Wang, W., et al. (2023) Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 3139-3153. [Google Scholar] [CrossRef] 
[13] He, J., Erfani, S., Ma, J., et al. (2021) Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression. arXiv: 2110.13675. [Google Scholar] [CrossRef] 
[14] Zhang, Q.-L. and Yang, Y.-B. (2021) SA-Net: Shuffle Attention for Deep Convolutional Neural Networks. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, 6-11 June 2021, 2235-2239. [Google Scholar] [CrossRef] 
[15] Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B. and Belongie, S. (2017) Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 936-944. [Google Scholar] [CrossRef] 
[16] Li, C., Li, L., Jiang, H., et al. (2022) YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv:2209.02976. [Google Scholar] [CrossRef] 
[17] Ge, Z., Liu, S., Wang, F., et al. (2021) YOLOX: Exceeding YOLO Series in 2021. arXiv: 2107.08430. [Google Scholar] [CrossRef] 
[18] Guo, M., Lu, C., Liu, Z., Cheng, M. and Hu, S. (2023) Visual Attention Network. Computational Visual Media, 9, 733-752. [Google Scholar] [CrossRef] 
[19] Lau, K.W., Po, L.-M. and Ur Rehman, Y.A. (2024) Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN. Expert Systems with Applications, 236, Article 121352. [Google Scholar] [CrossRef] 

为你推荐