计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230700117-7.doi: 10.11896/jsjkx.230700117
何鑫宇, 陆陈鑫, 冯书谊, 欧阳尚荣, 穆文涛
HE Xinyu, LU Chenxin, FENG Shuyi, OUYANG Shangrong, MU Wentao
摘要： 建设海洋强国是我国当前大力发展的战略方向。针对现有基于深度学习的遥感图像舰船目标检测识别算法在嵌入式平台上存在检测分类识别率低、运行速率慢等问题,提出了一种基于寒武纪MLU220嵌入式平台改进的Mix-YOLO网络模型。该模型以YOLOv7-tiny网络为基本框架,首先,引入MobileNet系列网络模块对特征提取网络进行部分替换,减少网络参数量;然后,引入ULSAM注意力机制,以便增强网络学习分类能力,减少虚警率;最后,为了显著提升嵌入式平台检测速率,采用将网络模型大模块拆分为小模块的编写方式。实验结果表明:Mix-YOLO算法在原YOLOv7-tiny网络基础上,参数量和计算量分别下降了39.70%和29.70%,处理帧率由97.27fps提升至120.88fps,精度提高了7.7％,能够实现对遥感图像中舰船目标的实时检测识别。
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| [1]GIRSHICK R.Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision(ICCV).Santiago,Chile.IEEE,2016:1440-1448. [2]JIANG H Z,LEARNED-MILLER E.Face Detection with the Faster R-CNN[C]//2017 12th IEEE International Conference on Automatic Face & Gesture Recognition(FG 2017).Washington,DC,USA.IEEE,2017:650-657. [3]HE K M,GKIOXARI G,DOLLÁR P,et al.Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision(ICCV).Venice,Italy,IEEE,2017:2980-2988. [4]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotMultiBox detector[J].Computer Vision,2016,1(14):21-37. [5]WANG Y Y,WANG C,ZHANG H,et al.Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery[J].Remote Sensing,2019,11(5):531. [6]REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas,NV,USA,2016:779-788. [7]REDMON J,FARHADI A.YOLO9000:Better,Faster,Stronger[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2017:6517-6525. [8]REDMON J,FARHADI A.Yolov3:An incre-mental improve-ment[J].arXiv:1804.02767,2018. [9]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020. [10]WANG C Y,BOCHKOVSKIY A,LIAO H Y M.YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//CVPR.2023:7464-7475. [11]CUI J H,ZHANG Y Z,WANG Z,et al.Light-weight object detection networks for embedded platform[J].Acta Optica Sinica,2019,39(4):0415006. [12]SU N,HUANG Z,YAN Y,et al.Detect Larger at Once:Large-Area Remote-Sensing Image Arbitrary-Oriented Ship Detection[J].IEEE Geoscience and Remote Sensing Letters,2022,19:1-5. [13]YU J,PENG X,LI S,et al.A Lightweight Ship Detection Me-thod in Optical Remote Sensing Image under Cloud Interference[C]//2021 IEEE International Instrumentation and Measurement Technology Conference(I2MTC).IEEE,2021. [14]NONG Y J,WANG J J.Remote sensing image caption method based on attention and reinforcement learning[J].Acta Optica Sinica,2021,41(22):2228001. [15]LIU T,ZHOU B J,ZHAO Y S,et al.Ship Detection Algorithm based on Improved YOLO V5[C]//2021 6th International Conference on Automation,Control and Robotics Engineering(CACRE).Dalian,China,2021:483-487. [16]WANG H Y,WANG C P,FU Q,et al.Lightweight Ship Detection Based on Optical Remote Sensing Images for Embedded Platform[J].Acta Optica Sinica,2023,43(12):1212001. [17]LIU X W,PIAO Y J,ZHENG L L,et al.Ship detection for complex scene images of space optical remote sensing[J].Optics and Precision Engineering,2023,31(6):892-904. [18]HOWARD A,SANDLER M,CHU G,et al.Searching for MobileNetV3[C]//Proceeding of the IEEE/CVF International Conference on Computer Vision.2019:1314-1324. [19]JIE H,LI S,GANG S.Squeeze-and-Excitation Networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2018. [20]SAINI R,JHA N K,DAS B,et al.ULSAM:Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.2020:1627-1636. |
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