Similar articles

Abstract: Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes. However, the distortion of objects during data acquisition and representation is seldom considered. In this paper, we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations. Based on this, a feature fusion framework is proposed to fully use the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data. Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline. In addition, the fusion strategies can be easily integrated into other detectors, such as the You Only Look Once (YOLO) series. The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments. Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.

基于多表征声呐数据特征融合的水下目标检测方法

[1]Ben Tamou A, Benzinou A, Nasreddine K, 2021. Multi-stream fish detection in unconstrained underwater videos by the fusion of two convolutional neural network detectors. Appl Intell, 51(8):5809-5821.

[2]Bochkovskiy A, Wang CY, Liao HYM, 2020. YOLOv4: optimal speed and accuracy of object detection. https://arxiv.org/abs/2004.10934

[3]Charles RQ, Su H, Mo KC, et al., 2017. PointNet: deep learning on point sets for 3D classification and segmentation. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.77-85.

[4]Chen K, Wang JQ, Pang JM, et al., 2019. MMDetection: open MMLab detection toolbox and benchmark. https://arxiv.org/abs/1906.07155

[5]Chen XL, Mu XQ, Guan J, et al., 2022. Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images. Front Inform Technol Electron Eng, 23(4):630-643.

[6]Chen ZH, Yang CHY, Li QF, et al., 2021. Disentangle your dense object detector. Proc 29^th ACM Int Conf on Multimedia, p.4939-4948.

[7]Feng CJ, Zhong YJ, Gao Y, et al., 2021. TOOD: task-aligned one-stage object detection. Proc IEEE/CVF Int Conf on Computer Vision, p.3490-3499.

[8]Ge Z, Liu ST, Wang F, et al., 2021. YOLOX: exceeding YOLO series in 2021.

[9]Ghiasi G, Lin TY, Le QV, 2019. NAS-FPN: learning scalable feature pyramid architecture for object detection. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.7029-7038.

[10]Girshick R, 2015. Fast R-CNN. Proc IEEE Int Conf on Computer Vision, p.1440-1448.

[11]Girshick R, Donahue J, Darrell T, et al., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.580-587.

[12]Girshick R, Donahue J, Darrell T, et al., 2016. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Patt Anal Mach Intell, 38(1):142-158.

[13]Huang H, Zhou H, Yang X, et al., 2019. Faster R-CNN for marine organisms detection and recognition using data augmentation. Neurocomputing, 337:372-384.

[14]Kim J, Yu SC, 2016. Convolutional neural network-based real-time ROV detection using forward-looking sonar image. Proc IEEE/OES Autonomous Underwater Vehicles, p.396-400.

[15]Kong WZ, Hong JC, Jia MY, et al., 2020. YOLOv3-DPFIN: a dual-path feature fusion neural network for robust real-time sonar target detection. IEEE Sens J, 20(7):‍3745-3756.

[16]Lin TY, Dollár P, Girshick R, et al., 2017. Feature pyramid networks for object detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.936-944.

[17]Liu D, Cheng F, 2021. SRM-FPN: a small target detection method based on FPN optimized feature. Proc 18^th Int Computer Conf on Wavelet Active Media Technology and Information Processing, p.506-509.

[18]Otsu N, 1979. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 9(1):62-66.

[19]Pu SL, Zhao W, Chen WJ, et al., 2021. Unsupervised object detection with scene-adaptive concept learning. Front Inform Technol Electron Eng, 22(5):638-651.

[20]Redmon J, Farhadi A, 2018. YOLOv3: an incremental improvement.

[21]Redmon J, Divvala S, Girshick R, et al., 2016. You Only Look Once: unified, real-time object detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.779-788.

[22]Ren SQ, He KM, Girshick R, et al., 2015. Faster R-CNN: towards real-time object detection with region proposal networks. Proc 28^th Int Conf on Neural Information Processing Systems, p.91-99.

[23]Song Y, He B, Liu P, 2021. Real-time object detection for AUVs using self-cascaded convolutional neural networks. IEEE J Oceanic Eng, 46(1):56-67.

[24]Sun PZ, Zhang RF, Jiang Y, et al., 2021. Sparse R-CNN: end-to-end object detection with learnable proposals. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.14449-14458.

[25]Tian MJ, Li XL, Kong SH, et al., 2022. A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot. Front Inform Technol Electron Eng, 23(8):‍1217-1228.

[26]Valdenegro-Toro M, 2016. Object recognition in forward-looking sonar images with convolutional neural networks. Proc OCEANS MTS/IEEE Monterey, p.1-6.

[27]Wang Z, Guo JX, Huang WZ, et al., 2022. Side-scan sonar image segmentation based on multi-channel fusion convolution neural networks. IEEE Sens J, 22(6):5911-5928.

[28]Yang GY, Wang ZY, Zhuang SN, 2021. PFF-FPN: a parallel feature fusion module based on FPN in pedestrian detection. Proc Int Conf on Computer Engineering and Artificial Intelligence, p.377-381.

[29]Zhang HK, Chang H, Ma BP, et al., 2020. Dynamic R-CNN: towards high quality object detection via dynamic training. Proc 16^th European Conf on Computer Vision, p.260-275.

[30]Zhou JC, Zhang DH, Ren WQ, et al., 2022a. Auto color correction of underwater images utilizing depth information. IEEE Geosci Remote Sens Lett, 19:1504805.

[31]Zhou JC, Yang TY, Chu WS, et al., 2022b. Underwater image restoration via backscatter pixel prior and color compensation. Eng Appl Artif Intell, 111:104785.