[2] Qing B. Application of radar technology in bird strike prevention[J]. China Occupational Safety and Health, 15, 52-53(2020).

[3] Chen W S, Zhang J, Lu X F. Airport bird situation analysis based on avian radar data[J]. China Civil Aviation, 43-45(2020).

[4] Lu J L, Yang W S, Shi Z Y et al. Design and realization of the airport bird detection system[J]. Laser & Infrared, 42, 775-777(2012).

[5] Zhang L M, Tu P, Wu W et al. On birdstrike disasters avoidance of Nanchang Qingyunpu airport[J]. Journal of Southwest China Normal University (Natural Science Edition), 42, 141-147(2017).

[6] Wu Y H, Liu W, Tai D Y et al. Avian community structure and bird strike prevention at Changzhou Benniu airport[J]. Chinese Journal of Wildlife, 41, 616-625(2020).

[7] Chen Y T, Liu Z G, Chen Y Q et al. Design of gateway in airport intelligent bird-repelling system[C], 2060-2064(2019).

[8] Cao Y X, Ding D W. Research on device for preventing bird-caused damage based on intelligent recognition and initiative driving[J]. Sichuan Electric Power Technology, 36, 78-80(2013).

[9] Ding J, Li L. Sparse synthesizing method for avian target based on compressed sensing[J]. Chinese Journal of Quantum Electronics, 32, 101-106(2015).

[10] Pan Y H, Wei J S, Zeng L P. Farmland bird detection algorithm based on YOLOv3[J]. Laser & Optoelectronics Progress, 59, 0233001(2022).

[11] Ma J J, Pan Q, Liang Y et al. Object detection based on improved Grassberger entropy random forest classifier[J]. Chinese Journal of Lasers, 46, 0233001(2019).

[12] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[13] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[14] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[15] Gao L Y, Sun D, Lu Y X. Face detection algorithm based on a lightweight attention mechanism network[J]. Laser & Optoelectronics Progress, 58, 0210010(2021).

[16] Bochkovskiy A, Wang C Y, Liao H Y M et al. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[17] Henriques J F, Caseiro R, Martins P et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583-596(2015).

[18] Chen F L, Ding Q H, Chang Z et al. Multi-scale kernel correlation filter algorithm for visual tracking based on the fusion of adaptive features[J]. Acta Optica Sinica, 40, 0315001(2020).

[19] Qi X M, Chen W. Correlation filter object tracking based on adaptive spatiotemporal regularization[J]. Laser & Optoelectronics Progress, 58, 0215007(2021).

[20] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[21] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422(2012).

[22] Wang Q, Gao J, Xing J L et al. DCFNET: discriminant correlation filters network for visual tracking[EB/OL]. https://arxiv.org/abs/1704.04057

[23] Zhu Z, Wang Q, Li B et al. Distractor-aware siamese networks for visual object tracking[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11213, 103-119(2018).