[1] Chen L, Liu Z H, Tong L et al. Underwater object detection using Invert Multi-Class Adaboost with deep learning[C](2020).
[2] Lin S, Zhao Y. Review on key technologies of target exploration in underwater optical images[J]. Laser & Optoelectronics Progress, 57, 060002(2020).
[3] Cutter G, Stierhoff K, Zeng J M. Automated detection of rockfish in unconstrained underwater videos using Haar cascades and a new image dataset: labeled fishes in the wild[C], 57-62(2015).
[4] Rizzini D L, Kallasi F, Oleari F et al. Investigation of vision-based underwater object detection with multiple datasets[J]. International Journal of Advanced Robotic Systems, 12, 77(2015).
[5] 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).
[6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).
[7] 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).
[8] Qiang W, He Y Y, Guo Y J et al. Exploring underwater target detection algorithm based on improved SSD[J]. Journal of Northwestern Polytechnical University, 38, 747-754(2020).
[9] Li Q Z, Li Y B, Niu J. Real-time detection of underwater fish based on improved YOLO and transfer learning[J]. Pattern Recognition and Artificial Intelligence, 32, 193-203(2019).
[10] Wang X, Guan Z Q, Wang J et al. Target detection of color image sonar based on convolutional neural network[J]. Journal of Computer Applications, 39, 187-191(2019).
[11] Liu Y Y, Zhang J M, Wang K P et al. Fast underwater target recognition with unbalanced data set[J]. Computer Engineering and Applications, 56, 236-242(2020).
[12] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850
[13] Zhang C Z, Kang B L, Li Y et al. Underwater image enhancement based on differential channel gain and improved Retinex[J]. Laser & Optoelectronics Progress, 58, 1410004(2021).
[16] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11215, 385-400(2018).
[17] Newell A, Yang K Y, Deng J. Stacked hourglass networks for human pose estimation[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9912, 483-499(2016).
[18] Liu X, Chen S Y, Chen X L et al. Deep multi-scale feature fusion target detection algorithm based on deep learning[J]. Laser & Optoelectronics Progress, 58, 1210029(2021).
[20] Dai J F, Qi H Z, Xiong Y W et al. Deformable convolutional networks[C], 764-773(2017).
[21] Zhang T, Zhang L. Multiscale feature fusion-based object detection algorithm[J]. Laser & Optoelectronics Progress, 58, 0215003(2021).
[22] Lin W H, Zhong J X, Liu S et al. ROIMIX: proposal-fusion among multiple images for underwater object detection[C], 2588-2592(2020).
[23] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).
[24] Law H, Deng J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).
[25] Zhou X Y, Zhuo J C, Krähenbühl P. Bottom-up object detection by grouping extreme and center points[C], 850-859(2019).