[1] Jaffe J S. Computer modeling and the design of optimal underwater imaging systems[J]. IEEE Journal of Oceanic Engineering, 15, 101-111(1990).

[2] Sun C D, Chen L Y, Gao L M et al. Water optical properties and their effect on underwater imaging[J]. Journal of Applied Optics, 21, 39-46(2000).

[3] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[4] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Computer vision-ECCV 2016, 9905, 21-37(2016).

[5] Ren S Q, He K M, Girshikck 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).

[6] Zhao Y A, Lü W Y, Xu S L et al. DETRs beat YOLOs on real-time object detection[EB/OL]. https://arxiv.org/abs/2304.08069v3

[7] Jiang L M, Chen X W. Visual SLAM algorithm based on feature point selection in dynamic scenes[J/OL]. Journal of System Simulation, 1-12. https://doi.org/10.16182/.joss.23-1406

[8] Song S J, Zhu J X. Research on underwater biological target recognition based on mask R-CNN and transfer learning[J]. Application Research of Computers, 37, 386-388, 391(2020).

[9] Wang R R, Jiang Z Y. Underwater object detection algorithm based on improved CenterNet[J]. Laser & Optoelectronics Progress, 60, 0215001(2023).

[10] Zhang M H, Xu S B, Song W et al. Lightweight underwater object detection based on YOLO v4 and multi-scale attentional feature fusion[J]. Remote Sensing, 13, 4706(2021).

[11] Yu K, Cheng Y F, Tian Z T et al. High speed and precision underwater biological detection based on the improved YOLOV4-tiny algorithm[J]. Journal of Marine Science and Engineering, 10, 1821(2022).

[12] Zhao L, Jiao L K, Zhai R et al. YOLOv5-based lightweight algorithm for detecting bottle-cap packaging defects[J]. Laser & Optoelectronics Progress, 60, 2210009(2023).

[13] Wu M M, Zhang Z B, Song Y Z et al. Small-target detection network based on adaptive feature enhancement[J]. Laser & Optoelectronics Progress, 60, 0610004(2023).

[14] Wu G H, Ge Y, Yang Q. UTD-YOLO: underwater trash detection model based on improved YOLOv5[J]. Journal of Electronic Imaging, 32, 063034(2023).

[15] Zhang J, Chen H D, Yan X Y et al. An improved YOLOv5 underwater detector based on an attention mechanism and multi-branch reparameterization module[J]. Electronics, 12, 2597(2023).

[16] Guo A, Sun K Q, Zhang Z Y. A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection[J]. Journal of Real-Time Image Processing, 21, 49(2024).

[17] Gatta C, Rizzi A, Marini D. ACE: an automatic color equalization algorithm[J]. Conference on Colour in Graphics, Imaging, and Vision, 1, 316-320(2002).

[18] Land E H, McCann J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 61, 1-11(1971).

[19] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2341-2353(2011).

[20] Rahman Z, Jobson D J, Woodell G A. Multi-scale retinex for color image enhancement[C], 1003-1006(2002).

[21] Narayanan M. SENetV2: aggregated dense layer for channelwise and global representations[EB/OL]. https://arxiv.org/abs/2311.10807v1

[22] Wang J Q, Chen K, Xu R et al. CARAFE: content-aware ReAssembly of FEatures[C], 3007-3016(2019).

[23] Liu W Z, Lu H, Fu H T et al. Learning to upsample by learning to sample[C], 6004-6014(2023).

[24] Dai X Y, Chen Y P, Xiao B et al. Dynamic head: unifying object detection heads with attentions[C], 7369-7378(2021).

[25] Rezatofighi H, Tsoi N, Gwak J et al. Generalized intersection over union: a metric and a loss for bounding box regression[C], 658-666(2019).

[26] 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).

[27] Zheng Z H, Wang P, Ren D W et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 52, 8574-8586(2022).

[28] Ma S L, Xu Y, Ma S L et al. MPDIoU: a loss for efficient and accurate bounding box regression[EB/OL]. https://arxiv.org/abs/2307.07662v1

[29] Zhang H, Xu C, Zhang S J. Inner-IoU: more effective intersection over union loss with auxiliary bounding box[EB/OL]. https://arxiv.org/abs/2311.02877v4