[5] JIANG P Y, ERGU D J, LIU F Y et al. A review of Yolo algorithm developments[J]. Procedia Computer Science, 199, 1066-1073(2022).

[6] MA Z L, WAN Y, LIU J J et al. A kind of water surface multi-scale object detection method based on improved YOLOv5 network[J]. Mathematics, 11, 2936(2023).

[8] CHEN F X, ZHANG L X, KANG S Y et al. Soft-NMS-enabled YOLOv5 with SIOU for small water surface floater detection in UAV-captured images[J]. Sustainability, 15, 10751(2023).

[9] [9] BEWLEY A, GE Z Y, OTT L, et al. Simple online realtime tracking[C]2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA: IEEE, 2016: 3464−3468.

[10] [10] WOJKE N, BEWLEY A, PAULUS D. Simple online realtime tracking with a deep association metric[C]2017 IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE, 2017: 3645−3649.

[11] [11] CHEN L, AI H Z, ZHUANG Z J, et al. Realtime multiple people tracking with deeply learned cidate ion person reidentification[C]2018 IEEE International Conference on Multimedia Expo (ICME). San Diego, CA, USA: IEEE, 2018: 1−6.

[12] ZHANG Y F, WANG C Y, WANG X G et al. FairMOT: on the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 129, 3069-3087(2021).

[13] [13] AHARON N, FAIG R, BOBROVSKY B Z. BoTST: robust associations multipedestrian tracking[JOL]. arXiv preprint arXiv: 2206.14651, 2022. https:arxiv.gabs2206.14651.

[15] [15] UKLEH A. Detekce a sledování objektů na vodní hladině[D]. Praze:České Vysoké Učení Technické v Praze. Vypočetní a Infmační Centrum, 2022.

[16] [16] CHENG Y W, ZHU J N, JIANG M X, et al. FloW: a dataset benchmark f floating waste detection in inl waters[C]Proceedings of the IEEECVF International Conference on Computer Vision. Montreal, QC, Canada: IEEE, 2021: 10953−10962.

[17] [17] ZHANG Y F, SUN P Z, JIANG Y, et al. ByteTrack: multiobject tracking by associating every detection box[C]17th European Conference on Computer Vision. Tel Aviv, Israel: Springer, 2022: 1−21.

[18] [18] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed accuracy of object detection[JOL]. arXiv preprint arXiv: 2004.10934, 2020. https:arxiv.gabs2004.10934.

[19] ZHENG Z H, WANG P, LIU W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).

[20] [20] WANG J W, XU C, YANG W, et al. A nmalized Gaussian Wasserstein distance f tiny object detection[JOL]. arXiv preprint arXiv: 2110.13389, 2021. https:arxiv.gabs2110.13389.

[22] [22] HE J B, ERFANI S, MA X J, et al. AlphaIoU: a family of power intersection over union losses f bounding box regression[JOL]. arXiv preprint arXiv: 2110.13675, 2021. https:arxiv.gabs2110.13675.