| Citation: |
XU Yebin, WANG Yunpeng, LIU Shaolong, LIU Li, LI Rui. ReNet: Ground Rotating Target Detection Method Based on Anchor-Free Frame[J]. Infrared Technology , 2025, 47(2): 211-216.
|
Ground infrared target detection is crucial in the fields of high-altitude reconnaissance, intelligent perception, and ground strike, where the acquired ground infrared targets often appear in the form of irregular angles, resulting in low detection accuracy, ease of misdetection, and other problems. Therefore, this paper proposes an anchor-free-based ground rotating target detection method. Based on the anchor-free target detection model, a backbone network based on atrous convolution is constructed, which enhances the perception range and feature extraction ability of the model for ground rotating targets. After feature extraction based on void convolution, the attention dimension of the extracted feature is increased through external attention, and the extraction of higher-resolution features of the target is realized. The ground rotating target detection model achieved 90.6% detection accuracy on the HIT-UAV dataset, which optimized the detection performance of the anchor-free target detection model for ground rotating targets.
| [1] |
REN S, HE K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015: 91-99.
|
| [2] |
Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
|
| [3] |
HE K, GKIOXARI G, DOLLÁR P, et al. Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
|
| [4] |
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.
|
| [5] |
赵晓枫, 吴飞, 徐叶斌, 等. 基于改进梯度相似度的红外隐身伪装评价方法[J]. 电光与控制, 2022, 29(2): 7-11.
ZHAO Xiaofeng, WU Fei, XU Yebin, et al. Infrared stealth camouflage evaluation method based on improved gradient similarity[J]. Electronics Optics & Control, 2022, 29(2): 7-11.
|
| [6] |
赵晓枫, 徐叶斌, 吴飞, 等. 基于并行注意力机制的地面红外目标检测方法(特邀)[J]. 红外与激光工程, 2022, 51(4): 100-107.
ZHAO Xiaofeng, XU Yebin, WU Fei, et al. Ground infrared target detection method based on a parallel attention mechanism[J]. Infrared and Laser Engineering: 2022, 51(4): 100-107.
|
| [7] |
赵晓枫, 牛家辉, 刘春桐, 等. 基于三维注意力与混合卷积的高光谱图像分类[J]. 系统工程与电子技术, 2023, 45(9): 2673-2680.
ZHAO Xiaofeng, NIU Jiahui, LIU Chuntong, et al. Hyperspectral image classification based on hybrid convolution with three-dimensional attention mechanism[J]. System Engineering and Electronics, 2023, 45(9): 2673-2680.
|
| [8] |
Law H, DENG J. Cornernet: Detecting objects as paired keypoints[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 734-750.
|
| [9] |
YU J, JIANG Y, WANGZ, et al. Unitbox: An advanced object detection network[C]//Proceedings of the 24th ACM International Conference on Multimedia, 2016: 516-520.
|
| [10] |
TIAN Z, SHEN C, CHEN H, et al. Fcos: fully convolutional one-stage object detection[C]// Proceedings of the IEEE international Conference on Computer Vision, 2019: 9627-9636.
|
| [11] |
YU C, WANG J, PENG C, et al. Bisenet: Bilateral segmentation network for real-time semantic segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 325-341.
|
| [12] |
YU C, WANG J, PENG C, et al. Learning a discriminative feature network for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 1857-1866.
|
| [13] |
吴洛冰, 谷玉海, 吴文昊, 等. 基于多尺度特征提取的遥感旋转目标检测[J]. 激光与光电子学进展, 2023, 60(12): 453-461.
WU L B, GU Y H, WU W H, et al. Remote sensing rotating object detection based on multi-scale feature extraction[J]. Laser & Optoelectronics Progress, 2023, 60(12): 453-461.
|
| [14] |
张聪. 基于卷积神经网络的旋转目标检测方法研究[D]. 成都: 电子科技大学, 2023.
ZHANG C. Research on Rotated Object Detection Method Based on Convolutional Neural Network[D]. Chengdu: University of Electronic Science and Technology Of China, 2023.
|
| [15] |
孙先涛, 闻勇, 陈文杰, 等. 基于语义分割与旋转目标检测的机器人抓取位姿估计[J/OL]. 控制与决策, 2024, 39(9): DOI: 10.13195/j.kzyjc.2023.0804.
SUN X T, WEN Y, CHEN W J, et al. Robot grasping pose estimation based on semantic segmentation and rotating target detection[J/OL]. Control and Decision, 2024, 39(9): DOI: 10.13195/j.kzyjc.2023.0804
|
| [16] |
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.
|
| [17] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141..
|
| [18] |
刘俊明, 孟卫华. 融合全卷积神经网络和视觉显著性的红外小目标检测[J]. 光子学报, 2020, 49(7): 46-56.
LIU Junming, MENG Weihua. Infrared small target detection based on fully convolutional neural network and visual saliency[J]. Acta Photonica Sinica, 2020, 49(7): 46-56.
|
| [19] |
DONG L, MA D, QIN G, et al. Infrared target detection in backlighting maritime environment based on visual attention model[J]. Infrared Physics & Technology, 2019, 99: 193-200.
|
| [20] |
SUO J, WANG T, ZHANG X, et al. HIT-UAV: a high-altitude infrared thermal dataset for unmanned aerial vehicle-based object detection[J/OL]. Sci Data 10, 2023, 227: https://doi.org/10.1038/s41597-023-02066-6.
|
| [21] |
GUO M H, LIU Z N, MU T J, et al. Beyond self-attention: external attention using two linear layers for visual tasks[J]. IEEE Trans. Pattern Anal Mach Intell., 2023, 45(5): 5436-5447.
|
| [22] |
DUAN K, BAI S, XIE L, et al. Centernet: keypoint triplets for object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2019: 6569-6578.
|
| [1] | YE Ye. A Deep Learning Method for Hyperspectral Detection of Heavy Metal Contaminants in Soil Based on Attention Mechanism[J]. Infrared Technology , 2025, 47(4): 453-458. |
| [2] | LI Ruihong, FU Zhitao, ZHANG Shaochen, ZHANG Jian, WANG Leiguang. Nighttime Object Detection in Infrared and Visible Images Based on Multi-Attention Mechanism[J]. Infrared Technology , 2024, 46(12): 1371-1379. |
| [3] | WANG Yan, ZHANG Jinfeng, WANG Likang, FAN Xianghui. Underwater Image Enhancement Based on Attention Mechanism and Feature Reconstruction[J]. Infrared Technology , 2024, 46(9): 1006-1014. |
| [4] | DI Jing, LIANG Chan, REN Li, GUO Wenqing, LIAN Jing. Infrared and Visible Image Fusion Based on Multi-Scale Contrast Enhancement and Cross-Dimensional Interactive Attention Mechanism[J]. Infrared Technology , 2024, 46(7): 754-764. |
| [5] | ZHAO Songpu, YANG Liping, ZHAO Xin, PENG Zhiyuan, LIANG Dongxing, LIANG Hongjun. Object Detection in Visible Light and Infrared Images Based on Adaptive Attention Mechanism[J]. Infrared Technology , 2024, 46(4): 443-451. |
| [6] | GAO Meiling, DUAN Jin, ZHAO Weiqiang, HU Qi. Near-infrared Image Colorization Method Based on a Dilated Global Attention Mechanism[J]. Infrared Technology , 2023, 45(10): 1096-1105. |
| [7] | LI Xiangrong, SUN Lihui. Multiscale Infrared Target Detection Based on Attention Mechanism[J]. Infrared Technology , 2023, 45(7): 746-754. |
| [8] | HE Le, LI Zhongwei, LUO Cai, REN Peng, SUI Hao. Infrared and Visible Image Fusion Based on Dilated Convolution and Dual Attention Mechanism[J]. Infrared Technology , 2023, 45(7): 732-738. |
| [9] | LUO Di, WANG Congqing, ZHOU Yongjun. A Visible and Infrared Image Fusion Method based on Generative Adversarial Networks and Attention Mechanism[J]. Infrared Technology , 2021, 43(6): 566-574. |
| [10] | WANG Hao, ZHANG Jingjing, LI Yuanyuan, WANG Feng, XUN Lina. Hyperspectral Image Classification Based on 3D Convolution Joint Attention Mechanism[J]. Infrared Technology , 2020, 42(3): 264-271. |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: