| Citation: |
YUE Mingkai, QUAN Kangnan, ZHANG Cong, HAN Ziqiang. Research on Infrared Small Target Detection Algorithm Based on Improved YOLOv8[J]. Infrared Technology , 2024, 46(11): 1286-1292.
|
Aiming at the problem of the high error rate of infrared small-target recognition and the large loss of model regression in complex backgrounds, an improved YOLOv8_SG (Small goals) algorithm was proposed by adding a small target detection layer and introducing the SA attention mechanism and WIoU_v3 loss function, which can fuse deeper features and have a larger receptive field. Moreover, the influence of the uneven labeling quality of the training samples was reduced, the position accuracy of the prediction box was improved, and the ability to detect small targets was enhanced. The experimental results show that the mAP of the improved algorithm increased from 0.8514 to 0.8997, and the overall loss effect of Box_loss increased by 34.9%. The proposed algorithm has a higher feature extraction ability and higher detection accuracy for small-target detection.
| [1] |
康鑫英, 张德育, 王君. 压缩SSD低空小目标检测算法研究[J]. 沈阳理工大学学报, 2022, 41(6): 1-5. DOI: 10.3969/j.issn.1003-1251.2022.06.001
KANG X Y, ZHANG D Y, WANG J. Research on detection algorithm of small target at low altitude based on compressed SSD[J]. Journal of Shenyang Ligong University, 2022, 41(6): 1-5. DOI: 10.3969/j.issn.1003-1251.2022.06.001
|
| [2] |
PENG H, ZHANG Y F, YANG S, et al. Battlefield image situational awareness application based on deep learning[J]. IEEE Intelligent Systems, 2020, 35(1): 36-43. DOI: 10.1109/MIS.2019.2953685
|
| [3] |
CHEN C Y, LIU M Y, TUZEL O, et al. R-CNN for small object detection[C]//Proceeding of Asian Conference on Computer Vision, 2016, 10115: 214-230.
|
| [4] |
PANG J M, CHEN K, SHI J P, et al. Libra R-CNN: towards balanced learning for object detection[J]. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 821-830.
|
| [5] |
王君, 张德育, 康鑫英. 改进Faster-RCNN的低空小型无人机检测方法[J]. 沈阳理工大学学报, 2021, 40(4): 23-28. DOI: 10.3969/j.issn.1003-1251.2021.04.005
WANG J, ZHANG D Y, KANG X Y. Improved detection method of low altitude small UAV by faster-RCNN[J]. Journal of Shenyang Ligong University, 2021, 40(4): 23-28. DOI: 10.3969/j.issn.1003-1251.2021.04.005
|
| [6] |
李嘉新, 侯进, 盛博莹, 等. 基于改进YOLOv5的遥感小目标检测网络[J]. 计算机工程, 2023, 9(2): 1-11.
LI J X, HOU J, SHENG B Y, et al. Remote sensing small object detection network based on improved YOLOv5[J]. Computer Engineering, 2023, 9(2): 1-11.
|
| [7] |
谢椿辉, 吴金明, 徐怀宇. 改进YOLOv5的无人机影像小目标检测算法[J]. 计算机工程与应用, 2023, 59(9): 198-206.
XIE C H, WU J M, XU H Y. Small object detection algorithm based on improved YOLOv5 in UAV image[J]. Computer Engineering and Applications, 2023, 59(9): 198-206.
|
| [8] |
倪浩鹏. 基于深度学习的红外小目标检测关键算法研究[D]. 南京: 南京邮电大学, 2022.
NI H P. Research on Critical Algorithms of Infrared Small Target Detection Based on Deep Learning[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2022.
|
| [9] |
张艳珠, 包慧哲, 于长海, 等. 基于无人机图像地面建筑物目标检测算法研究[J]. 沈阳理工大学学报, 2023, 42(4): 1-6, 14. DOI: 10.3969/j.issn.1003-1251.2023.04.001
ZHANG Y Z, BAO H Z, YU C H, et al. Research on the algorithm for detecting objects on the ground based on images by UAV[J]. Journal of Shenyang Ligong University, 2023, 42(4): 1-6, 14. DOI: 10.3969/j.issn.1003-1251.2023.04.001
|
| [10] |
商俊燕. 基于深度学习的遥感图像微小目标检测方法研究[J]. 计算机测量与控制, 2022, 30(10): 57-62.
SHANG J Y. Research on remote sensing image micro target detection method based on deep learning[J]. Computer Measurement & Control, 2022, 30(10): 57-62.
|
| [11] |
贺顺, 谢永妮, 杨志伟, 等. 基于IHBF的增强局部对比度红外小目标检测方法[J]. 红外技术, 2022, 44(11): 1132-1138. http://hwjs.nvir.cn/article/id/0f2609dc-79df-467e-ac1d-4d5f888850d1
HE S, XIE Y N, YANG Z W, et al. IHBF-based enhanced local contrast measure method for infrared small target detection[J]. Infrared Technology, 2022, 44(11): 1132-1138. http://hwjs.nvir.cn/article/id/0f2609dc-79df-467e-ac1d-4d5f888850d1
|
| [12] |
张丹, 王军锋. 多尺度加权图像块局部对比度的红外小目标检测[J]. 计算机工程与设计, 2023, 44(5): 1459-1470.
ZHANG D, WANG J F. Infrared small target detection based on multi-scale weighted local contrast[J]. Computer Engineering and Design, 2023, 44(5): 1459-1470.
|
| [13] |
蒲家鹏, 王雪梅, 高宏伟. 基于改进YOLOv4的航拍图像目标检测方法研究[J]. 沈阳理工大学学报, 2023, 42(3): 46-53.
PU J P, WANG X M, GAO H W. Research of aerial image object detection method based on improved YOLOv4[J]. Journal of Shenyang Ligong University, 2023, 42(3): 46-53.
|
| [14] |
ZHANG Q L, YANG Y B. SA-Net: shuffle attention for deep convolutional neural networks[J]. IEEE International Conference on Acoustics, 2021, 30(1): 2235-2239.
|
| [15] |
刘浩翰, 孙铖, 贺怀清, 等. 基于改进YOLOv3的金属表面缺陷检测[J]. 计算机工程与科学, 2023, 45(7): 1226-1235. DOI: 10.3969/j.issn.1007-130X.2023.07.010
LIU H H, SUN C, HE H Q, et al. Metal surface defect detection based on improved YOLOv3[J]. Computer Engineering & Science, 2023, 45(7): 1226-1235. DOI: 10.3969/j.issn.1007-130X.2023.07.010
|
| [16] |
HAN Z Q, ZHANG C, FENG H Z, et al. PFFNET: a fast progressive feature fusion network for detecting drones in infrared images[J]. Drones, 2023, 7(7): 424. DOI: 10.3390/drones7070424
|
| [17] |
SHUANG J D, ZHANG B F, ZHANG B, et al. An improved bounding box regression loss function based on CIOU loss for multi-scale object detection[C]// IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML), 2021: 92-98.
|
| [18] |
刘雄彪, 杨贤昭, 陈洋, 等. 基于CIoU改进边界框损失函数的目标检测方法[J]. 液晶与显示, 2023, 38(5): 656-665.
LIU X B, YANG X Z, CHEN Y, et al. Object detection method based on CIoU improved bounding box loss function[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(5): 656-665.
|
| [19] |
陈益方, 张上, 冉秀康, 等. 基于改进YOLOv8的SAR图像飞机目标检测算法[J]. 电讯技术, 2023, 9(2): 1-8.
CHEN Y F, ZHANG S, RAN X K, et al. Aircraft target detection algorithm based on improved YOLOv8 in SAR image[J]. Telecommunication Engineering, 2023, 9(2): 1-8.
|
| [20] |
许晓阳, 高重阳. 改进YOLOv7-tiny的轻量级红外车辆目标检测算法[J]. 计算机工程与应用, 2023, 9(2): 1-13.
XU X Y, GAO C Y. Improved YOLOv7-Tiny lightweight infrared vehicle target detection algorithm[J]. Computer Engineering and Applications, 2023, 9(2): 1-13.
|
| [21] |
孙晓亮, 郭良超, 张文龙, 等. 复杂背景下红外弱小运动目标检测半仿真数据集[EB/OL]. [2023-08-15]. 2021. https://doi.org/10.11922/sciencedb.j00001.00231.
SUN X L, GUO L C, ZHANG W L, et al. A dataset for small infrared moving target detection under clutter background[EB/OL]. [2023-08-15]. 2021. https://doi.org/10.11922/sciencedb.j00001.00231.
|
| [22] |
马学森, 马吉, 蒋功辉, 等. 基于注意力机制和多尺度特征融合的绝缘子缺陷检测方法[J]. 南京大学学报: 自然科学, 2022, 58(6): 1020-1029.
MA X S, MA J, JIANG G H, et al. Insulator defect detection method based on attention mechanism and multi-scale feature fusion[J]. Journal of Nanjing University: Natural Science, 2022, 58(6): 1020-1029.
|
| [1] | LI Xiangrong, SUN Lihui. Multiscale Infrared Target Detection Based on Attention Mechanism[J]. Infrared Technology , 2023, 45(7): 746-754. |
| [2] | LONG Zhiliang, DENG Yueming, WANG Runmin, DONG Jun. Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation[J]. Infrared Technology , 2023, 45(7): 705-713. |
| [3] | ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512. |
| [4] | HU Jiahui, ZHAN Weida, GUI Tingting, SHI Yanli, GU Xing. Infrared Image Enhancement Method Based on Multiscale Weighted Guided Filtering[J]. Infrared Technology , 2022, 44(10): 1082-1088. |
| [5] | CHEN Wenyi, YANG Chengxun, YANG Hui. Multiscale Retinex Infrared Image Enhancement Based on the Fusion of Guided Filtering and Logarithmic Transformation Algorithm[J]. Infrared Technology , 2022, 44(4): 397-403. |
| [6] | YE Kuntao, LI Wen, SHU Leilei, LI Sheng. Infrared and Visible Image Fusion Method Based on Improved Saliency Detection and Non-subsampled Shearlet Transform[J]. Infrared Technology , 2021, 43(12): 1212-1221. |
| [7] | LI Chuandong, XU Wangming, WU Shiqian. Real-Time Pedestrian Detection Based on the Weak Saliency Map in Thermal Infrared Images[J]. Infrared Technology , 2021, 43(7): 658-664. |
| [8] | JIAO Anbo, HE Miao, LUO Haibo. Research on Significant Edge Detection of Infrared Image Based on Deep Learning[J]. Infrared Technology , 2019, 41(1): 72-77. |
| [9] | LIU Hui, SHI Xiaolong. Improved GrabCut Segmentation Based on Salience and Superpixels[J]. Infrared Technology , 2018, 40(1): 55-61. |
| [10] | FAN Ming-zhe, WANG Lu-ping, ZHANG Lu-ping. Regional Detection Algorithm Based on Double Error Reconstruction[J]. Infrared Technology , 2015, 37(11): 962-969. |
| 1. |
靳铁柱，刘生彦. 改进背景减法下人体运动模糊图像检测仿真. 计算机仿真. 2025(03): 304-308 .
| |
| 2. |
王封疆，王梦飞，周杰. 基于CHEBWO的多目标棉田图像增强算法. 石河子大学学报(自然科学版). 2024(04): 505-513 .
| |
| 3. |
张海庆. 不同天气条件下光学图像清晰度实时增强研究. 自动化与仪器仪表. 2024(11): 39-42+47 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: