Joint attention mechanism for the design of anti-bird collision accident detection system

Xuanfeng Li; Jiajia Yu; Xuanfeng Li; Jiajia Yu

doi:10.3934/era.2022223

Electronic Research Archive

2022, Volume 30, Issue 12: 4401-4415. doi: 10.3934/era.2022223

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Research article Special Issues

Joint attention mechanism for the design of anti-bird collision accident detection system

Xuanfeng Li ,
Jiajia Yu ^,

Department of Electronic and Computer Engineering, Southeast University Chengxian College, Nanjing 210088, China

Received: 19 July 2022 Revised: 15 September 2022 Accepted: 27 September 2022 Published: 08 October 2022

- bird collision accident,
- flight safety,
- YOLOv5s improvement,
- SE module,
- CBAM module,
- TAL,
- W_i
Citation: Xuanfeng Li, Jiajia Yu. Joint attention mechanism for the design of anti-bird collision accident detection system[J]. Electronic Research Archive, 2022, 30(12): 4401-4415. doi: 10.3934/era.2022223

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References

[1]	D. Aleksandra, I. Cavka, V. Cokorilo, Bird strikes on an aircraft and bird strike prevention, Tehnika, 2 (2014), 291–298. https://doi.org/10.5937/tehnika1402291D doi: 10.5937/tehnika1402291D
[2]	A. T. Marques, H. Batalha, J. Bernardino, Bird displacement by wind turbines: assessing current knowledge and recommendations for future studies, Birds, 2 (2021), 460–475. https://doi.org/10.3390/birds2040034 doi: 10.3390/birds2040034
[3]	T. Wu, X. Luo, Q. Xu, A new skeleton based flying bird detection method for low-altitude air traffic management, Chin. J. Aeronaut., 31 (2018), 2149–2164. https://doi.org/10.1016/j.cja.2018.01.018 doi: 10.1016/j.cja.2018.01.018
[4]	X. Zhang, X. Wu, X. Zhou, X. Wang, Y. Zhang, Automatic detection and tracking of maneuverable birds in videos, in 2008 International Conference on Computational Intelligence and Security, (2008), 185–189. https://doi.org/10.1109/CIS.2008.46
[5]	K. A. Klein, R. Mino, M. J. Hovan, P. Antonik, G. Genello, MMW radar for dedicated bird detection at airports and airfields, in First European Radar Conference, (2004), 157–160.
[6]	T. Yang, S. Z. Li, O. Pan, J. Li, Real-time and accurate segmentation of moving objects in dynamic scene, in Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks, (2004), 136–143. https://doi.org/10.1145/1026799.1026822
[7]	Q. Hu, R. Li, Y. Xu, C. Pan, C. Niu, W. Liu, Toward aircraft detection and fine-grained recognition from remote sensing images, J. Appl. Remote Sens., 16 (2022), 024516. https://doi.org/10.1117/1.JRS.16.024516 doi: 10.1117/1.JRS.16.024516
[8]	X. Yang, X. Fan, J. Wang, X. Yin, S. Qiu, Edge-based cover recognition and tracking method for an AR-aided aircraft inspection system, Int. J. Adv. Manuf. Technol., 111 (2020), 3505–3518. https://doi.org/10.1007/s00170-020-06301-x doi: 10.1007/s00170-020-06301-x
[9]	J. Yi, P. Wu, B. Liu, Q. Huang, H. Qu, D. Metaxas, Oriented object detection in aerial images with box boundary-aware vectors, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), (2021), 2149–2158. https://doi.org/10.1109/WACV48630.2021.00220
[10]	J. Feng, D. Ming, B. Zeng, J. Yu, Y. Qing, T. Du, et al., Aircraft detection in high spatial resolution remote sensing images combining multi-angle features driven and majority voting CNN, Remote Sens., 13 (2021), 2207–2224. https://doi.org/10.3390/RS13112207 doi: 10.3390/RS13112207
[11]	C. Wang, A. Bochkovskiy, H. M. Liao, Scaled-yolov4: Scaling cross stage partial network, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 13024–13033. https://doi.org/10.1109/CVPR46437.2021.01283
[12]	Q. Hou, D. Zhou, J. Feng, Coordinate attention for efficient mobile network design, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 13708–13717. https://doi.org/10.1109/CVPR46437.2021.01350
[13]	T. ting, L. Guo, H. Gao, T. Chen, Y. Yu, C. Li, A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring, Int. J. Adv. Manuf. Technol., 120 (2022), 5633–5648. https://doi.org/10.1007/S00170-022-09032-3 doi: 10.1007/S00170-022-09032-3
[14]	H. Gong, L. Chen, H. Pan, S. Li, Y. Guo, L. Fu, et al., Sika deer facial recognition model based on SE-ResNet, Comput. Mater. Con., 72 (2022), 6015–6027. https://doi.org/10.32604/CMC.2022.027160 doi: 10.32604/CMC.2022.027160
[15]	H. Fu, G. Song, Y. Wang, Improved YOLOv4 marine target detection combined with CBAM, Symmetry, 13 (2021), 623–637. https://doi.org/10.3390/sym13040623 doi: 10.3390/sym13040623
[16]	Z. Li, X. Huang, Z. Zhang, L. Liu, F. Wang, S. Li, Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function, Quant. Imag. Med. Surg., 12 (2022), 3151–3169. https://doi.org/10.21037/qims-21-846 doi: 10.21037/qims-21-846
[17]	D. Zhu, Overlapping boundary based multimedia slice transcoding method and its system for medical video and traffic video, Multimed. Tools Appl., 75 (2016), 14233–14246. https://doi.org/10.1007/s11042-015-3235-8 doi: 10.1007/s11042-015-3235-8
[18]	Z. Tang, J. Li., Y. Zhou, Clothing information collection based on theme web crawler, Int. J. Adv. Networking Appl., 10 (2019), 3919–3924. https://doi.org/10.35444/IJANA.2019.10043 doi: 10.35444/IJANA.2019.10043
[19]	J. Huang, D. Shao, H. Liu, Y. Xiang, L. Ma, S. Yi, et al., A lightweight segmentation method based on residual U-Net for MR images, J. Intell. Fuzzy Syst., 42 (2022), 5085–5095. https://doi.org/10.3233/JIFS-211424 doi: 10.3233/JIFS-211424
[20]	C. Wen, M. Hong, X. Yang, J. Jia, Pulmonary nodule detection based on convolutional block attention module, in 2019 Chinese Control Conference (CCC), (2019), 8583–8587. https://doi.org/10.23919/ChiCC.2019.8865792
[21]	H. Alqaysi, I. Fedorov, F. Z. Qureshi, M. O'Nils, A temporal boosted YOLO-Based model for birds detection around wind farms, J. Imaging, 7 (2021), 227–240. https://doi.org/10.3390/jimaging7110227 doi: 10.3390/jimaging7110227

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