Infrared small target detection (ISTD) is the main research content for defense confrontation, long-range precision strikes and battlefield intelligence reconnaissance. Targets from the aerial view have the characteristics of small size and dim signal. These characteristics affect the performance of traditional detection models. At present, the target detection model based on deep learning has made huge advances. The You Only Look Once (YOLO) series is a classic branch. In this paper, a model with better adaptation capabilities, namely ISTD-YOLOv7, is proposed for infrared small target detection. First, the anchors of YOLOv7 are updated to provide prior. Second, Gather-Excite (GE) attention is embedded in YOLOv7 to exploit feature context and spatial location information. Finally, Normalized Wasserstein Distance (NWD) replaces IoU in the loss function to alleviate the sensitivity of YOLOv7 for location deviations of small targets. Experiments on a standard dataset show that the proposed model has stronger detection performance than YOLOv3, YOLOv5s, SSD, CenterNet, FCOS, YOLOXs, DETR and the baseline model, with a mean Average Precision (mAP) of 98.43%. Moreover, ablation studies indicate the effectiveness of the improved components.
Citation: Kangjian Sun, Ju Huo, Qi Liu, Shunyuan Yang. An infrared small target detection model via Gather-Excite attention and normalized Wasserstein distance[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19040-19064. doi: 10.3934/mbe.2023842
| [1] | Mengshi Shu, Rui Fu, Wendi Wang . A bacteriophage model based on CRISPR/Cas immune system in a chemostat. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1361-1377. doi: 10.3934/mbe.2017070 |
| [2] | Miller Cerón Gómez, Eduardo Ibarguen Mondragon, Eddy Lopez Molano, Arsenio Hidalgo-Troya, Maria A. Mármol-Martínez, Deisy Lorena Guerrero-Ceballos, Mario A. Pantoja, Camilo Paz-García, Jenny Gómez-Arrieta, Mariela Burbano-Rosero . Mathematical model of interaction Escherichia coli and Coliphages. Mathematical Biosciences and Engineering, 2023, 20(6): 9712-9727. doi: 10.3934/mbe.2023426 |
| [3] | Frédéric Mazenc, Gonzalo Robledo, Daniel Sepúlveda . A stability analysis of a time-varying chemostat with pointwise delay. Mathematical Biosciences and Engineering, 2024, 21(2): 2691-2728. doi: 10.3934/mbe.2024119 |
| [4] | Gonzalo Robledo . Feedback stabilization for a chemostat with delayed output. Mathematical Biosciences and Engineering, 2009, 6(3): 629-647. doi: 10.3934/mbe.2009.6.629 |
| [5] | Harry J. Dudley, Zhiyong Jason Ren, David M. Bortz . Competitive exclusion in a DAE model for microbial electrolysis cells. Mathematical Biosciences and Engineering, 2020, 17(5): 6217-6239. doi: 10.3934/mbe.2020329 |
| [6] | Xiaomeng Ma, Zhanbing Bai, Sujing Sun . Stability and bifurcation control for a fractional-order chemostat model with time delays and incommensurate orders. Mathematical Biosciences and Engineering, 2023, 20(1): 437-455. doi: 10.3934/mbe.2023020 |
| [7] | Frédéric Mazenc, Michael Malisoff, Patrick D. Leenheer . On the stability of periodic solutions in the perturbed chemostat. Mathematical Biosciences and Engineering, 2007, 4(2): 319-338. doi: 10.3934/mbe.2007.4.319 |
| [8] | Manel Dali Youcef, Alain Rapaport, Tewfik Sari . Study of performance criteria of serial configuration of two chemostats. Mathematical Biosciences and Engineering, 2020, 17(6): 6278-6309. doi: 10.3934/mbe.2020332 |
| [9] | Alain Rapaport, Jérôme Harmand . Biological control of the chemostat with nonmonotonic response and different removal rates. Mathematical Biosciences and Engineering, 2008, 5(3): 539-547. doi: 10.3934/mbe.2008.5.539 |
| [10] | Alexis Erich S. Almocera, Sze-Bi Hsu, Polly W. Sy . Extinction and uniform persistence in a microbial food web with mycoloop: limiting behavior of a population model with parasitic fungi. Mathematical Biosciences and Engineering, 2019, 16(1): 516-537. doi: 10.3934/mbe.2019024 |
Infrared small target detection (ISTD) is the main research content for defense confrontation, long-range precision strikes and battlefield intelligence reconnaissance. Targets from the aerial view have the characteristics of small size and dim signal. These characteristics affect the performance of traditional detection models. At present, the target detection model based on deep learning has made huge advances. The You Only Look Once (YOLO) series is a classic branch. In this paper, a model with better adaptation capabilities, namely ISTD-YOLOv7, is proposed for infrared small target detection. First, the anchors of YOLOv7 are updated to provide prior. Second, Gather-Excite (GE) attention is embedded in YOLOv7 to exploit feature context and spatial location information. Finally, Normalized Wasserstein Distance (NWD) replaces IoU in the loss function to alleviate the sensitivity of YOLOv7 for location deviations of small targets. Experiments on a standard dataset show that the proposed model has stronger detection performance than YOLOv3, YOLOv5s, SSD, CenterNet, FCOS, YOLOXs, DETR and the baseline model, with a mean Average Precision (mAP) of 98.43%. Moreover, ablation studies indicate the effectiveness of the improved components.
| [1] |
B. Jiang, X. Ma, Y. Lu, Y. Li, L. Feng, Z. Shi, Ship detection in spaceborne infrared images based on Convolutional Neural Networks and synthetic targets, Infrared Phys. Technol., 97 (2019), 229–234. https://doi.org/10.1016/j.infrared.2018.12.040 doi: 10.1016/j.infrared.2018.12.040
|
| [2] |
A. Özdil, B. Yılmaz, Automatic body part and pose detection in medical infrared thermal images, Quant. InfraRed Thermogr. J., 19 (2021), 223–238. https://doi.org/10.1080/17686733.2021.1947595 doi: 10.1080/17686733.2021.1947595
|
| [3] |
F. Prata, Detection and avoidance of atmospheric aviation hazards using infrared spectroscopic imaging, Remote Sens., 12 (2020), 2309. https://doi.org/10.3390/rs12142309 doi: 10.3390/rs12142309
|
| [4] |
C. Gao, L. Wang, Y. Xiao, Q. Zhao, D. Meng, Infrared small-dim target detection based on Markov random field guided noise modelling, Pattern Recognit., 76 (2018), 463–475. https://doi.org/10.1016/j.patcog.2017.11.016 doi: 10.1016/j.patcog.2017.11.016
|
| [5] |
M. Qi, L. Liu, S. Zhuang, Y. Liu, K. Li, Y. Yang, et al., FTC-Net: Fusion of transformer and CNN features for infrared small target detection, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15 (2022), 8613–8623. https://doi.org/10.1109/JSTARS.2022.3210707 doi: 10.1109/JSTARS.2022.3210707
|
| [6] |
N. Nguyen, T. Do, T. Ngo, D. Le, An evaluation of deep learning methods for small object detection, J. Electr. Comput. Eng., 2020 (2020), 3189691. https://doi.org/10.1155/2020/3189691 doi: 10.1155/2020/3189691
|
| [7] | R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, (2014), 580–587. https://doi.org/10.1109/CVPR.2014.81 |
| [8] |
J. Li, X. Liang, S. Shen, T. Xu, J. Feng, S. Yan, Scale-aware fast R-CNN for pedestrian detection, IEEE Trans. Multimedia, 20 (2017), 985–996. https://doi.org/10.1109/TMM.2017.2759508 doi: 10.1109/TMM.2017.2759508
|
| [9] |
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39 (2017), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031 doi: 10.1109/TPAMI.2016.2577031
|
| [10] |
K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask R-CNN, IEEE Trans. Pattern Anal. Mach. Intell., 42 (2020), 386–397. https://doi.org/10.1109/TPAMI.2018.2844175 doi: 10.1109/TPAMI.2018.2844175
|
| [11] |
P. Jiang, D. Ergu, F. Liu, Y. Cai, B. Ma, A review of YOLO algorithm developments, Procedia Comput. Sci., 199 (2022), 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135 doi: 10.1016/j.procs.2022.01.135
|
| [12] | J. Redmon, A. Farhadi, YOLOv3: An incremental improvement, preprint, arXiv: 1804.02767. |
| [13] |
S. Shen, X. Zhang, W. Yan, S. Xie, B. Yu, S. Wang, An improved UAV target detection algorithm based on ASFF-YOLOv5s, Math. Biosci. Eng., 20 (2023), 10773–10789. https://doi.org/10.3934/mbe.2023478 doi: 10.3934/mbe.2023478
|
| [14] | Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun, YOLOX: Exceeding YOLO series in 2021, preprint, arXiv: 2107.08430. |
| [15] | C. Wang, A. Boschkovskiy, H. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, preprint, arXiv: 2207.0269. |
| [16] |
M. Soeb, M. Jubayer, T. Tarin, M. Mamun, F. Ruhad, A. Parven, et al., Tea leaf disease detection and identification based on YOLOv7 (YOLO-T), Sci. Rep., 13 (2023), 6078. https://doi.org/10.1038/s41598-023-33270-4 doi: 10.1038/s41598-023-33270-4
|
| [17] |
S. Li, J. Yu, H. Wang, Damages detection of aeroengine blades via deep learning algorithms, IEEE Trans. Instrum. Meas., 72 (2023), 1–11. https://doi.org/10.1109/TIM.2023.3249247 doi: 10.1109/TIM.2023.3249247
|
| [18] |
S. Liu, Y. Wang, Q. Yu, H. Liu, Z. Peng, CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection, IEEE Access, 10 (2022), 129116–129124. https://doi.org/10.1109/ACCESS.2022.3228331 doi: 10.1109/ACCESS.2022.3228331
|
| [19] |
F. Chen, C. Gao, F. Liu, Y. Zhao, Y. Zhou, D. Meng, et al., Local patch network with global attention for infrared small target detection, IEEE Trans. Aerosp. Electron. Syst., 58 (2022), 3979–3991. https://doi.org/10.1109/TAES.2022.3159308 doi: 10.1109/TAES.2022.3159308
|
| [20] |
Y. Dai, Y. Wu, F. Zhou, K. Barnard, Attentional local contrast networks for infrared small target detection, IEEE Trans. Geosci. Remote Sens., 59 (2021), 9813–9824. https://doi.org/10.1109/TGRS.2020.3044958 doi: 10.1109/TGRS.2020.3044958
|
| [21] | M. Zhang, R. Zhang, Y. Yang, H. Bai, J. Zhang, J. Guo, ISNet: Shape matters for infrared small target detection, in Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022), 867–876. https://doi.org/10.1109/CVPR52688.2022.00095 |
| [22] |
B. Li, C. Xiao, L. Wang, Y. Wang, Z. Lin, M. Li, W. An, et al., Dense nested attention network for infrared small target detection, IEEE Trans. Image Process., 32 (2023), 1745–1758. https://doi.org/10.1109/TIP.2022.3199107 doi: 10.1109/TIP.2022.3199107
|
| [23] |
T. Wu, B. Li, Y. Luo, Y. Wang, C. Xiao, T. Liu, et al., MTU-Net: Multilevel TransUNet for space-based infrared tiny ship detection, IEEE Trans. Geosci. Remote Sens., 61 (2023), 1–15, Art no. 5601015. https://doi.org/10.1109/TGRS.2023.3235002 doi: 10.1109/TGRS.2023.3235002
|
| [24] |
Z. Lin, B. Li, M. Li, L. Wang, T. Wu, Y. Luo, et al., Light-weight infrared small target detection combining cross-scale feature fusion with bottleneck attention module, J. Infrared Millimeter Waves, 41 (2022), 1102–1112. https://doi.org/10.11972/j.issn.1001-9014.2022.06.020 doi: 10.11972/j.issn.1001-9014.2022.06.020
|
| [25] | Y. Liu, X. Wang, SAR ship detection based on improved YOLOv7-Tiny, in Proceedings of the 2022 IEEE 8th International Conference on Computer and Communications, (2022), 2166–2170. https://doi.org/10.1109/ICCC56324.2022.10065775 |
| [26] |
Y. Guo, S. Chen, R. Zhan, W. Wang, J. Zhang, LMSD-YOLO: A lightweight YOLO algorithm for multi-scale SAR ship detection, Remote Sens., 14 (2022), 4801. https://doi.org/10.3390/rs14194801 doi: 10.3390/rs14194801
|
| [27] |
X. Zhou, L. Jiang, C. Hu, S. Lei, T. Zhang, X. Mou, YOLO-SASE: An improved YOLO algorithm for the small targets detection in complex backgrounds, Sensors, 22 (2022), 4600. https://doi.org/10.3390/s22124600 doi: 10.3390/s22124600
|
| [28] | VOC dataset, Available from: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/. |
| [29] | COCO dataset, Available from: http://cocodataset.org/#download. |
| [30] | J. Hu, L. Shen, S. Albanie, G. Sun, A. Vedaldi, Gather-Excite: Exploiting feature context in convolutional neural networks, in Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18), (2018), 9423–9433. |
| [31] | J. Wang, C. Xu, W. Yang, L. Yu, A normalized Gaussian Wasserstein distance for tiny object detection, preprint, arXiv: 2110.13389. |
| [32] |
C. Xu, J. Wang, W. Yang, H. Yu, L. Yu, G. Xia, Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark, ISPRS J. Photogramm. Remote Sens., 190 (2022), 79–93. https://doi.org/10.1016/j.isprsjprs.2022.06.002 doi: 10.1016/j.isprsjprs.2022.06.002
|
| [33] |
H. Lai, L. Chen, W. Liu, Z. Yan, S. Ye, STC-YOLO: Small object detection network for traffic signs in complex environments, Sensors, 23 (2023), 5307. https://doi.org/10.3390/s23115307 doi: 10.3390/s23115307
|
| [34] |
Z. Zheng, N. Chen, J. Wu, Z. Xv, S. Liu, Z. Luo, EW-YOLOv7: A lightweight and effective detection model for small defects in electrowetting display, Processes, 11 (2023), 2037. https://doi.org/10.3390/pr11072037 doi: 10.3390/pr11072037
|
| [35] | J. Hosang, R. Benenson, B. Schiele, Learning non-maximum suppression, in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 6469–6477. https://doi.org/10.1109/CVPR.2017.685 |
| [36] |
R. Fu, H. Fan, Y. Zhu, B. Hui, Z. Zhang, P. Zhong, et al., A dataset for infrared time-sensitive target detection and tracking for air-ground application, China Sci. Data, 7 (2022), 206–221. https://doi.org/10.11922/sciencedb.j00001.00331 doi: 10.11922/sciencedb.j00001.00331
|
| [37] |
C. Chen, G. Yuan, H. Zhou, Y. Ma, Improved YOLOv5s model for key components detection of power transmission lines, Math. Biosci. Eng., 20 (2023), 7738–7760. https://doi.org/10.3934/mbe.2023334 doi: 10.3934/mbe.2023334
|
| [38] |
M. Huang, Y. Wu, GCS-YOLOV4-Tiny: A lightweight group convolution network for multi-stage fruit detection, Math. Biosci. Eng., 20 (2023), 241–268. https://doi.org/10.3934/mbe.2023011 doi: 10.3934/mbe.2023011
|
| [39] | W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, et al., SSD: Single Shot MultiBox Detector, in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, (2016), 21–37. https://doi.org/10.1007/978-3-319-46448-0_2 |
| [40] | K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, Q. Tian, CenterNet: Keypoint triplets for object detection, in Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), (2019), 6568–6577. https://doi.org/10.1109/ICCV.2019.00667 |
| [41] | Z. Tian, C. Shen, H. Chen, T. He, FCOS: Fully convolutional one-stage object detection, in Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), (2019), 9626–9635. https://doi.org/10.1109/ICCV.2019.00972 |
| [42] | N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko, End-to-end object detection with transformers, in Proceedings of the Computer Vision—ECCV 2020, (2020), 213–229. https://doi.org/10.1007/978-3-030-58452-8_13 |
| [43] | I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial networks, preprint, arXiv: 1406.2661. |
| 1. | Xinzhi Ren, Xianning Liu, A competition un-stirred chemostat model with virus in an aquatic system, 2019, 98, 0003-6811, 2329, 10.1080/00036811.2018.1460811 | |
| 2. | Wendi Wang, Rui Fu, Mengshi Shu, A bacteriophage model based on CRISPR/Cas immune system in a chemostat, 2017, 14, 1551-0018, 1361, 10.3934/mbe.2017070 | |
| 3. | Saptarshi Sinha, Rajdeep K. Grewal, Soumen Roy, 2018, 103, 9780128151839, 103, 10.1016/bs.aambs.2018.01.005 | |
| 4. | Saptarshi Sinha, Rajdeep Kaur Grewal, Soumen Roy, 2020, Chapter 18, 978-1-0716-0388-8, 309, 10.1007/978-1-0716-0389-5_18 | |
| 5. | Sukhitha W. Vidurupola, Analysis of deterministic and stochastic mathematical models with resistant bacteria and bacteria debris for bacteriophage dynamics, 2018, 316, 00963003, 215, 10.1016/j.amc.2017.08.022 | |
| 6. | Daniel A. Korytowski, Hal L. Smith, How nested and monogamous infection networks in host-phage communities come to be, 2015, 8, 1874-1738, 111, 10.1007/s12080-014-0236-6 | |
| 7. | Saroj Kumar Sahani, Sunita Gakkhar, A Mathematical Model for Phage Therapy with Impulsive Phage Dose, 2020, 28, 0971-3514, 75, 10.1007/s12591-016-0303-0 | |
| 8. | Sukhitha W. Vidurupola, Linda J. S. Allen, Impact of Variability in Stochastic Models of Bacteria-Phage Dynamics Applicable to Phage Therapy, 2014, 32, 0736-2994, 427, 10.1080/07362994.2014.889922 | |
| 9. | WENDI WANG, DYNAMICS OF BACTERIA-PHAGE INTERACTIONS WITH IMMUNE RESPONSE IN A CHEMOSTAT, 2017, 25, 0218-3390, 697, 10.1142/S0218339017400010 | |
| 10. | Hayriye Gulbudak, Paul L. Salceanu, Gail S. K. Wolkowicz, A delay model for persistent viral infections in replicating cells, 2021, 82, 0303-6812, 10.1007/s00285-021-01612-3 | |
| 11. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Htoo Kyaw Hlaing, Stability analysis and persistence of a phage therapy model, 2021, 18, 1551-0018, 5552, 10.3934/mbe.2021280 | |
| 12. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Stability and Hopf Bifurcation Analysis for a Phage Therapy Model with and without Time Delay, 2023, 12, 2075-1680, 772, 10.3390/axioms12080772 | |
| 13. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Hopf bifurcation analysis of a phage therapy model, 2023, 18, 2157-5452, 87, 10.2140/camcos.2023.18.87 | |
| 14. | Zainab Dere, N.G. Cogan, Bhargav R. Karamched, Optimal control strategies for mitigating antibiotic resistance: Integrating virus dynamics for enhanced intervention design, 2025, 00255564, 109464, 10.1016/j.mbs.2025.109464 | |
| 15. | Carli Peterson, Darsh Gandhi, Austin Carlson, Aaron Lubkemann, Emma Richardson, John Serralta, Michael S. Allen, Souvik Roy, Christopher M. Kribs, Hristo V. Kojouharov, A SIMPL Model of Phage-Bacteria Interactions Accounting for Mutation and Competition, 2025, 87, 0092-8240, 10.1007/s11538-025-01478-2 |
Kangjian Sun, Ju Huo, Qi Liu, Shunyuan Yang. An infrared small target detection model via Gather-Excite attention and normalized Wasserstein distance[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19040-19064. doi: 10.3934/mbe.2023842
DownLoad: