Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv6

doi:10.3778/j.issn.1002-8331.2304-0270

Abstract

Abstract: Aiming at the low target detection accuracy caused by complex background of remote sensing images, generally small targets and multi-scale distribution of targets, a remote sensing image target detection algorithm based on improved YOLOv6 is proposed. Firstly, a coordinate attention module is introduced into the backbone network to improve the feature extraction ability and target location ability of the model under complex background. Secondly, a context enhancement module is proposed to enable the model to obtain rich context information, so as to improve the ability of model to extract multi-scale target details. Finally, in order to realize the adaptive fusion of different scale features, an adaptive spatial feature fusion is introduced into the neck network to improve the detection accuracy of multi-scale targets, especially small targets. The proposed algorithm is trained and tested on DOTA-v1.0, an open data set of remote sensing images. The experimental results show that the convergence speed and convergence accuracy of the improved algorithm are better than that of the original algorithm, and the AP value reaches 54.6%, which is 1.4 percentage points higher than that of the original algorithm. Meantime, compared with some other advanced target detection algorithms, accuracy and speed is improved which demonstrates the effectiveness of the improved algorithm.

Key words: remote sensing image, target detection, attention mechanism, multi-scale target, YOLOv6

摘要： 针对遥感图像背景复杂、目标普遍比较小且呈多尺度分布所导致的目标检测精度较低的问题，提出了一种改进YOLOv6的遥感图像目标检测算法。在骨干网络引入一种坐标注意力模块，以提高复杂背景下模型的特征提取能力和目标定位能力。提出一种上下文增强模块，使模型获取丰富的上下文信息，从而提升模型提取多尺度目标细节信息的能力。为了实现不同尺度特征的自适应融合，通过在颈网络引入一种自适应空间特征融合，提升了多尺度目标尤其是小目标的检测精度。将所提改进算法在遥感图像公开数据集DOTA-v1.0上进行训练并测试，实验结果表明，改进算法的收敛速度与收敛精度均优于原算法，其中AP值达到了54.6%，相比原算法提高了1.4个百分点，同时相比于一些其他目前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。

关键词: 遥感图像, 目标检测, 注意力机制, 多尺度目标, YOLOv6

XU Degang, WANG Zaiqing, XING Kuijie, GUO Yixin. Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv6[J]. Computer Engineering and Applications, 2024, 60(3): 119-128.

许德刚, 王再庆, 邢奎杰, 郭奕欣. 改进YOLOv6的遥感图像目标检测算法[J]. 计算机工程与应用, 2024, 60(3): 119-128.

References

[1] 蔡肖, 陈志华, 盛斌. 基于移位窗口金字塔Transformer的遥感图像目标检测[J]. 计算机科学, 2023, 50(1): 105-113.
CAI X, CHEN Z H, SHENG B. SPT: swin pyramid Transformer for object detection of remote sensing[J]. Computer Science, 2019, 50(1): 105-113.
[2] 范新南, 严炜, 史朋飞, 等. 多尺度深度特征融合网络的遥感图像目标检测[J]. 遥感学报, 2022, 26(11): 2292-2303.
FAN X N, YAN W, SHI P F, et al. Remote sensing image target detection based on a multi-scale deep feature fusion network[J]. National Remote Sensing Bulletin, 2022, 26(11): 2292-2303.
[3] OSUNA E, FREUND R, GIROSIT F. Training support vector machines: an application to face detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1997: 130-136.
[4] VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001: 511-518.
[5] KAYASAL U. Magnetometer aided inertial navigation system: modeling and simulation of a navigation system with an IMU and a magnetometer[M]. Turkey: National Defense Industry Press, 2009: 74-77.
[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, 2014: 580-587.
[7] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1440-1448.
[8] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[9] HE K, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2961-2969.
[10] REDMON J, FARHADI A. Yolov3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[11] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[12] GLENN J. YOLOv5[EB/OL]. (2020). https://github.com/ultralytics/yolov5.
[13] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[14] 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.
[15] 汪西莉, 梁正印, 刘涛. 基于特征注意力金字塔的遥感图像目标检测方法[J]. 遥感学报, 2023, 27(2): 492-501.
WANG X L, LIANG Z Y, LIU T. Feature attention pyramid-based remote sensing image object detection method[J]. National Remote Sensing Bulletin, 2023, 27(2): 492-501.
[16] 赵珊, 郑爱玲, 刘子路, 等. 通道分离双注意力机制的目标检测算法[J]. 计算机科学与探索, 2023, 17(5): 1112-1125.
ZHAO S, ZHENG A L, LIU Z L, et al. Object detection algorithm based on channel separation dual attention mechanism[J]. Journal of Frontiers of Computer Science and Technology, 2019, 17(5): 1112-1125.
[17] 贾天豪, 彭力, 戴菲菲. 引入残差学习与多尺度特征增强的目标检测器[J]. 计算机科学与探索, 2023, 17(5): 1102-1111.
JIA T H, PENG L, DAI F F. Object detector with residual learning and multi-scale feature enhancement[J]. Journal of Frontiers of Computer Science and Technology, 2019, 17(5): 1102-1111.
[18] 李坤亚, 欧鸥, 刘广滨, 等. 改进YOLOv5的遥感图像目标检测算法[J]. 计算机工程与应用, 2023, 59(9): 207-214.
LI K Y, OU O, LIU G B, et al. Target detection algorithm of remote sensing image based on improved YOLOv5[J]. Computer Engineering and Applications, 2023, 59(9): 207-214.
[19] 杨晨, 佘璐, 杨璐, 等. 改进YOLOv5的遥感影像目标检测算法[J]. 计算机工程与应用, 2023, 59(15): 76-86.
YANG C, SHE L, YANG L, et al. Improved YOLOv5 object detection algorithm for remote sensing images[J]. Computer Engineering and Applications, 2023, 59(15): 76-86.
[20] 刘涛, 丁雪妍, 张冰冰, 等. 改进YOLOv5的遥感图像检测方法[J]. 计算机工程与应用, 2023, 59(10): 253-261.
LIU T, DING X Y, ZHANG B B, et al. Improved YOLOv5 for remote sensing image detection [J]. Computer Engineering and Applications , 2023, 59(10): 253-261.
[21] 汪鹏, 辛雪静, 王利琴, 等. 基于YOLOv3的光学遥感图像目标检测算法[J]. 激光与光电子学进展, 2021, 58(20): 509-517.
WANG P, XIN X J, WANG L Q, et al. Object detection algorithm of optical remote sensing images based on YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(20): 509-517.
[22] DONG X, QIN Y, GAO Y, et al. Attention-based multi-level feature fusion for object detection in remote sensing images[J]. Remote Sensing, 2022, 14(15): 3735.
[23] WANG J, GONG Z, LIU X, et al. Object detection based on adaptive feature-aware method in optical remote sensing images[J]. Remote Sensing, 2022, 14(15): 3616.
[24] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.02976, 2022.
[25] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[26] LIU S, HUANG D, WANG Y. Learning spatial fusion for single-shot object detection[J]. arXiv:1911.09516, 2019.
[27] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.
[28] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.
[29] XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3974-3983.
[30] ZHANG Q L, YANG Y B. Sa-net: shuffle attention for deep convolutional neural networks[C]//2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021: 2235-2239.
[31] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[32] FENG C, ZHONG Y, GAO Y, et al. Tood: task-aligned one-stage object detection[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021: 3490-3499.
[33] CHEN Z, YANG C, LI Q, et al. Disentangle your dense object detector[C]//Proceedings of the 29th ACM International Conference on Multimedia, 2021: 4939-4948.
[34] GE Z, LIU S, WANG F, et al. Yolox: exceeding YOLO series in 2021[J]. arXiv:2107.08430, 2021.