Tiny bird detection and location guided by heterogeneous binocular images in transformer substation scene

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Bird activities like nesting and perching in transformer substations threaten power grid stability by causing short circuits and insulation failures. Existing bird repelling devices are inefficient due to the lack of accurate detection and positioning, leading to energy waste and safety hazards from continuous operation. To address this, this paper develops a tiny bird detection and location system guided by heterogeneous binocular images for precise, targeted repulsion. For well-lit scenarios, a two-stage contextual information enhancement network is proposed. It mines multiscale context to highlight tiny bird regions, fuses context with second-stage features via channel dimension enhancement, and uses spatial attention for accurate localization. For low-light or occluded scenes, a multiscale contextual feature enhancement network processes infrared images, adopting multibranch cross-level feature fusion and combining transformer with multisize convolution to suppress background and thermal radiation interference. Additionally, heterogeneous binocular cameras are calibrated to calculate bird spatial distance, integrating detection results with spatial information to drive a laser repelling device. Experimental results in real substation environments show the system meets engineering requirements for robustness and accuracy. The detection in visible images achieves an overall average precision of 59.8%, while the infrared detection outperforms advanced algorithms in key metrics. The spatial localization error is controlled within 4.9%, significantly improving bird expulsion success rate and reducing energy consumption. This work provides a reliable technical solution for safeguarding power grid operation and offers valuable references for tiny object detection in complex industrial scenarios.

Keywords

tiny bird detection / object location / bird repelling / heterogeneous binocular vision / transformer substation

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] H. Zhou, H. Zhang, A. Wang, X. Fang, H. Xu, Y. Song, Application of intelligent bird repelling technology for power transmission and transformation equipment, in 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), (2024), 289-293. https://doi.org/10.1109/ICPICS62053.2024.10796329

[2] Q. Chen, J. Xie, Q. Yu, C. Liu, W. Ding, X. Li, et al., An experimental study of acoustic bird repellents for reducing bird encroachment in pear orchards, Front. Plant Sci., 15 (2024), 1365275. https://doi.org/10.3389/fpls.2024.1365275

[3] S. Shanmugapriya, S. Srikanth, S. Stanley, S. Surya, Intelligent IoT system for animal detection and crop defense using acoustic and visual repellents, in 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), (2025), 1-5. https://doi.org/10.1109/AIMLA63829.2025.11040691

[4] Y. Chen, J. Chu, K. Hsieh, T. Lin, P. Chang, Y. Tsai, Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism, Sci. Rep., 14 (2024), 15924. https://doi.org/10.1038/s41598-024-66920-2

[5] J. Wu, Z. Pan, B. Lei, Y. Hu, FSANet: Feature-and-spatial-aligned network for tiny object detection in remote sensing images, IEEE Trans. Geosci. Remote Sens., 60 (2022), 1-17. https://doi.org/10.1109/TGRS.2022.3205052

[6] D. Liu, J. Zhang, Y. Qi, Y. Wu, Y. Zhang, A tiny object detection method based on explicit semantic guidance for remote sensing images, IEEE Geosci. Remote Sens. Lett., 21 (2024), 1-5. https://doi.org/10.1109/LGRS.2024.3374418

[7] L. Xu, Y. Song, W. Zhang, Y. An, Y. Wang, H. Ning, An efficient foreign objects detection network for power substation, Image Vision Comput., 109 (2021), 104159. https://doi.org/10.1016/j.imavis.2021.104159

[8] J. Ou, J. Wang, J. Xue, J. Wang, X. Zhou, L. She, et al., Infrared image target detection of substation electrical equipment using an improved faster R-CNN, IEEE Trans. Power Delivery, 38 (2023), 387-396. https://doi.org/10.1109/TPWRD.2022.3191694

[9] S. Qiao, L. Chen, A. Yuille, DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021), 10208-10219. https://doi.org/10.1109/CVPR46437.2021.01008

[10] M. Yue, L. Zhang, J. Huang, H. Zhang, Lightweight and efficient tiny-object detection based on improved yolov8n for UAV aerial images, Drones, 8 (2024), 276. https://doi.org/10.1109/aiac63745.2024.10899550

[11] B. Mahaur, K. K. Mishra, A. Kumar, An improved lightweight small object detection framework applied to real-time autonomous driving, Expert Syst. Appl., 234 (2023), 121036. https://doi.org/10.1016/j.eswa.2023.121036

[12] Z. Zhao, J. Du, C. Li, X. Fang, Y. Xiao, J. Tang, Dense tiny object detection: A scene context guided approach and a unified benchmark, IEEE Trans. Geosci. Remote Sens., 62 (2024), 1-13. https://doi.org/10.1109/TGRS.2024.3357706

[13] J. Xiao, H. Guo, J. Zhou, T. Zhao, Q. Yu, Y. Chen, et al., Tiny object detection with context enhancement and feature purification, Expert Syst. Appl., 211 (2023), 118665. https://doi.org/10.1016/j.eswa.2022.118665

[14] S. Huang, C. Lin, X. Jiang, Z. Qu, BRSTD: Bio-inspired remote sensing tiny object detection, IEEE Trans. Geosci. Remote Sens., 62 (2024), 1-15. https://doi.org/10.1109/TGRS.2024.3470900

[15] H. Liu, Y. Tseng, K. Chang, P. Wang, H. Shuai, W. Cheng, A denoising FPN with transformer R-CNN for tiny object detection, IEEE Trans. Geosci. Remote Sens., 62 (2024), 1-15. https://doi.org/10.1109/TGRS.2024.3396489

[16] J. Leng, Y. Ren, W. Jiang, X. Sun, Y. Wang, Realize your surroundings: Exploiting context information for small object detection, Neurocomputing, 433 (2021), 287-299. https://doi.org/10.1016/j.neucom.2020.12.093

[17] D. Ma, B. Liu, Q. Huang, Q. Zhang, MwdpNet: Towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image, Sci. Rep., 13 (2023), 13890. https://doi.org/10.1038/s41598-023-41021-8

[18] C. Xu, J. Ding, J. Wang, W. Yang, H. Yu, L. Yu, et al., Dynamic coarse-to-fine learning for oriented tiny object detection, in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2023), 7318-7328. https://doi.org/10.1109/CVPR52729.2023.00707

[19] D. Liu, J. Zhang, Y. Qi, Y. Wu, Y. Zhang, A tiny object detection method based on explicit semantic guidance for remote sensing images, IEEE Trans. Geosci. Remote Sens., 21 (2024), 1-5. https://doi.org/10.1109/LGRS.2024.3374418

[20] C. W. Corsel, M. van Lier, L. Kampmeijer, N. Boehrer, E. M. Bakker, Exploiting temporal context for tiny object detection, in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) , (2023), 1-11. https://doi.org/10.1109/WACVW58289.2023.00013

[21] B. Li, C. Xiao, L. Wang, Y. Wang, Z. Lin, M. Li, et al., Dense nested attention network for infrared small target detection, IEEE Trans. Image Process., 32 (2022), 1745-1758. https://doi.org/10.1109/TIP.2022.3199107

[22] X. Wu, D. Hong, J. Chanussot, UIU-Net: U-Net in U-Net for infrared small object detection, IEEE Trans. Image Process., 32 (2023), 364-376. https://doi.org/10.1109/TIP.2022.3228497

[23] T. Zhang, L. Li, S. Cao, T. Pu, Z. Peng, Attention-guided pyramid context networks for detecting infrared small target under complex background, IEEE Trans. Aerosp. Electron. Syst., 59 (2023), 4250-4261. https://doi.org/10.1109/TAES.2023.3238703

[24] L. Huang, S. Dai, T. Huang, X. Huang, H. Wang, Infrared small target segmentation with multiscale feature representation, Infrared Phys. Technol., 116 (2021), 103755. https://doi.org/10.1016/j.infrared.2021.103755

[25] X. Xiong, K. Wang, J. Chen, T. Li, B. Lu, F. Ren, A calibration system of intelligent driving vehicle mounted scene projection camera based on zhang zhengyou calibration method, in 2022 34th Chinese Control and Decision Conference (CCDC), (2022), 747-750. https://doi.org/10.1109/CCDC55256.2022.10034031

[26] 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

[27] J. Hu, L. Shen, S. Albanie, G. Sun, E. Wu, Squeeze-and-excitation networks, IEEE Trans. Pattern Anal. Mach. Intell., 42 (2020), 2011-2023. https://doi.org/10.1109/TPAMI.2019.2913372

[28] F. Chollet, Xception: Deep learning with depthwise separable convolutions, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 1800-1807. https://doi.org/10.1109/CVPR.2017.195

[29] X. Chen, Y. Zhang, Y. Dong, B. Du, Spatial-spectral contrastive self-supervised learning with dual path networks for hyperspectral target detection, IEEE Trans. Geosci. Remote Sens., 62 (2024), 1-12. https://doi.org/10.1109/TGRS.2024.3390946

[30] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, et al., Swin transformer: Hierarchical vision transformer using shifted windows, in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), (2021), 9992-10002. https://doi.org/10.1109/ICCV48922.2021.00986

[31] 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

[32] Z. Tian, C. Shen, H. Chen, T. He, FCOS: A simple and strong anchor-free object detector, IEEE Trans. Pattern Anal. Mach. Intell., 44 (2022), 1922-1933. https://doi.org/10.1109/TPAMI.2020.3032166

[33] Z. Cai, N. Vasconcelos, Cascade R-CNN: Delving into high quality object detection, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 6154-6162. https://doi.org/10.1109/CVPR.2018.00644

[34] K. Sun, B. Xiao, D. Liu, J. Wang, Deep high-resolution representation learning for human pose estimation, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , (2019), 5686-5696. https://doi.org/10.1109/CVPR.2019.00584

[35] T. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, Focal loss for dense object detection, IEEE Trans. Pattern Anal. Mach. Intell., 42 (2020), 318-327. https://doi.org/10.1109/TPAMI.2018.2858826

[36] J. Terven, D. Cordova-Esparza, J. Romero-Gonzalez, A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS, Mach. Learn. Knowl. Extr., 5 (2023), 1680-1716. https://doi.org/10.3390/make5040083

[37] Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun, YOLOX: Exceeding YOLO series in 2021, preprint, arXiv:2107.08430.

[38] C. Xu, J. Wang, W. Yang, H. Yu, L. Yu, G. Xia, RFLA: Gaussian receptive field based label assignment for tiny object detection, in Computer Vision-ECCV 2022, (2022), 526-543. https://doi.org/10.1007/978-3-031-20077-9_31

[39] C. Xu, J. Wang, W. Yang, L. Yu, Dot distance for tiny object detection in aerial images, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , (2021), 1192-1201. https://doi.org/10.1109/CVPRW53098.2021.00130

[40] X. Yuan, G. Cheng, K. Yan, Q. Zeng, J. Han, Small object detection via coarse-to-fine proposal generation and imitation learning, in 2023 IEEE/CVF International Conference on Computer Vision (ICCV), (2023), 6294-6304. https://doi.org/10.1109/ICCV51070.2023.00581

[41] Y. Dai, Y. Wu, F. Zhou, K. Barnard, Asymmetric contextual modulation for infrared small target detection, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) , (2021), 949-958. https://doi.org/10.1109/WACV48630.2021.00099

[42] 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

[43] Q. Hou, L. Zhang, F. Tan, Y. Xi, H. Zheng, N. Li, ISTDU-Net: Infrared small-target detection U-Net, IEEE Geosci. Remote Sens. Lett., 19 (2022), 1-5. https://doi.org/10.1109/LGRS.2022.3141584

[44] K. Wang, S. Du, C. Liu, Z. Cao, Interior attention-aware network for infrared small target detection, IEEE Trans. Geosci. Remote Sens., 60 (2022), 1-13. https://doi.org/10.1109/TGRS.2022.3163410

[45] H. Sun, J. Bai, F. Yang, X. Bai, Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset irdst, IEEE Trans. Geosci. Remote Sens., 61 (2023), 1-13. https://doi.org/10.1109/TGRS.2023.3235150

[46] K. Oksuz, B. C. Cam, E. Akbas, S. Kalkan, Localization recall precision (LRP): A new performance metric for object detection, in Computer Vision-ECCV 2018, 11211 (2018), 521-537. https://doi.org/10.1007/978-3-030-01234-2_31

[47] J. Wang, W. Yang, H. Guo, R. Zhang, G. Xia, Tiny object detection in aerial images, in 2020 25th International Conference on Pattern Recognition (ICPR), (2021), 3791-3798. https://doi.org/10.1109/ICPR48806.2021.9413340