基于GA-RetinaNet的水下目标检测
作者:
基金项目:

河南省科技研发项目(212102210078); 河南省重大科技专项(201300210400); 河南省重点研发与推广专项(科技攻关)(202102210380)


Underwater Object Detection Based on GA-RetinaNet
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [32]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    水下目标自动检测方法对海洋智能捕捞工作发挥着重要作用, 针对现有目标检测方法存在的对水下生物检测精度不高问题, 提出了一种GA-RetinaNet算法的水下目标检测方法. 首先, 针对水下图像存在密集目标的特点, 通过引入分组卷积替换普通卷积, 在不增加参数复杂度的基础上得到更多特征图, 提高模型的检测精度; 其次, 根据水下生物多为小目标生物的特点, 引入上下文特征金字塔模块(AC-FPN), 利用上下文提取模块保证高分辨率输入的同时获得多个感受野, 提取到更多上下文信息, 并通过上下文注意力模块和内容注意力模块从中捕获有用特征, 准确定位到目标位置. 实验结果显示, 选用URPC2021数据集进行实验, 改进的GA-RetinaNet算法比原算法检测精度提高了2.3%. 相比其他主流模型, 该算法对不同类型的水下目标均获得了较好的检测结果, 检测精度有较大提升.

    Abstract:

    Automatic underwater object detection methods play an important role in intelligent marine fishing. To address the problem that the existing object detection methods are not accurate enough for underwater biological detection, this study proposes an underwater object detection method based on the GA-RetinaNet algorithm. Firstly, according to the existence of dense objects in underwater images, the study introduces group convolution to replace ordinary convolution, which can provide more feature information without increasing the complexity of parameters and thereby improve the accuracy of the model. Secondly, according to the characteristic that underwater objects are mostly small objects, the attention-guided context feature pyramid network (AC-FPN) is introduced. The context extraction module is used to obtain more receptive fields while guaranteeing high-resolution inputs and thus extract more contextual information. The context attention module and the content attention module are utilized to capture useful features for the accurate positioning of the object. Experimental results show that the improved GA-RetinaNet algorithm enhances the detection accuracy by 2.3% compared with the original algorithm when the URPC2021 dataset is selected. Compared with other mainstream models, the GA-RetinaNet algorithm achieves better detection results for different types of underwater objects, and the detection accuracy is greatly improved.

    参考文献
    [1] 张志锋, 贺蓉, 吴大千, 等. 我国海洋生态文明建设和生态环境保护进展、形势与思考. 环境与可持续发展, 2022, 47(3): 3–6. [doi: 10.19758/j.cnki.issn1673-288x.202203003
    [2] 刘晓阳, 杨润贤, 高宁. 水下机器人发展现状与发展趋势探究. 科技创新与生产力, 2018, (6): 19–20. [doi: 10.3969/j.issn.1674-9146.2018.06.019
    [3] Papageorgiou C, Poggio T. A trainable system for object detection. International Journal of Computer Vision, 2000, 38(1): 15–33. [doi: 10.1023/A:1008162616689
    [4] Olmos A, Trucco E. Detecting man-made objects in unconstrained subsea videos. Proceedings of the 2002 British Machine Conference. Cardiff: BMVA Press, 2002. 517–526.
    [5] Barat C, Phlypo R. A fully automated method to detect and segment a manufactured object in an underwater color image. EURASIP Journal on Advances in Signal Processing, 2010, 2010: 10
    [6] Chuang MC, Hwang JN, Williams K. A feature learning and object recognition framework for underwater fish images. IEEE Transactions on Image Processing, 2016, 25(4): 1862–1872
    [7] 王慧斌, 张倩, 王鑫, 等. 基于区域显著度与水下光学先验的目标检测. 仪器仪表学报, 2014, 35(2): 387–397
    [8] 马国强, 田云臣, 李晓岚. K-均值聚类算法在海水背景石斑鱼彩色图像分割中的应用. 计算机应用与软件, 2016, 33(5): 192–195. [doi: 10.3969/j.issn.1000-386x.2016.05.048
    [9] Zhu YF, Chang L, Dai JL, et al. Automatic object detection and segmentation from underwater images via saliency-based region merging. Proceedings of the OCEANS 2016. Shanghai: IEEE, 2016. 1–4.
    [10] Zou ZX, Chen KY, Shi ZW, et al. Object detection in 20 years: A survey. Proceedings of the IEEE, 2023: 1–20. [doi: 10.1109/JPROC.2023.3238524
    [11] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014. 580–587.
    [12] Girshick R. Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015. 1440–1448.
    [13] Ren SQ, He KM, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2015. 91–99.
    [14] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 779–788.
    [15] Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector. Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer, 2016. 21–37.
    [16] Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection. Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017. 2999–3007.
    [17] Zhou H, Huang H, Yang X, et al. Faster R-CNN for marine organism detection and recognition using data augmentation. Proceedings of the 2017 International Conference on Video and Image Processing. Singapore: ACM, 2017. 56–62.
    [18] Chen L, Liu ZH, Tong L, et al. Underwater object detection using invert multi-class AdaBoost with deep learning. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020. 1–8.
    [19] 赵力, 宋威. 基于非对称双分支交互神经网络的水下生物识别. 计算机应用研究, 2021, 38(4): 1240–1244, 1255
    [20] Fan BJ, Chen W, Cong Y, et al. Dual refinement underwater object detection network. Proceedings of the 16th European Conference on Computer Vision. Glasgow: Springer, 2020. 275–291.
    [21] Liu H, Song PH, Ding RW. Towards domain generalization in underwater object detection. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP). Abu Dhabi: IEEE, 2020. 1971–1975.
    [22] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 770–778.
    [23] Lin TY, Dollár P, Girshick R, et al. Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 936–944.
    [24] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015. 3431–3440.
    [25] Oksuz K, Cam BC, Kalkan S, et al. Imbalance problems in object detection: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3388–3415. [doi: 10.1109/TPAMI.2020.2981890
    [26] Ioannou Y, Robertson D, Cipolla R, et al. Deep roots: Improving CNN efficiency with hierarchical filter groups. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 5977–5986.
    [27] Duan KW, Du DW, Qi HG, et al. Detecting small objects using a channel-aware deconvolutional network. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(6): 1639–1652. [doi: 10.1109/TCSVT.2019.2906246
    [28] Cao JX, Chen Q, Guo J, et al. Attention-guided context feature pyramid network for object detection. arXiv: 2005.11475, 2020.
    [29] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 2261–2269.
    [30] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122, 2015.
    [31] Dai JF, Qi HZ, Xiong YW, et al. Deformable convolutional networks. Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017. 764–773.
    [32] Bao WX, Fan T, Hu GS, et al. Detection and identification of tea leaf diseases based on AX-RetinaNet. Scientific Reports, 2022, 12(1): 2183. [doi: 10.1038/s41598-022-06181-z
    引证文献
引用本文

袁明阳,宋亚林,张潮,沈兴盛,李世昌.基于GA-RetinaNet的水下目标检测.计算机系统应用,2023,32(6):80-90

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-12-05
  • 最后修改日期:2023-01-06
  • 在线发布日期: 2023-04-07
文章二维码
您是第11202862位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号