Computer Science ›› 2019, Vol. 46 ›› Issue (8): 332-336.doi: 10.11896/j.issn.1002-137X.2019.08.055

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Ship Target Detection Based on Improved YOLO v2

YU Yang, LI Shi-jie, CHEN Liang, LIU Yun-ting   

  1. (School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China)
  • Received:2018-06-05 Online:2019-08-15 Published:2019-08-15

Abstract: Aiming at the problem of low target detection accuracy and poor system robustness in ship image target detection,an improved YOLO v2 algorithm was proposed to detect ship image targets.The traditional YOLO v2 algorithm is improved by clustering the target frame dimension,optimizing the network structure,multi-scale transformation of input image,so as to better adapt to the ship target detection task.The test results show that the mean Average Precision (mAP)of the algorithm is 79.1% when the input image size is 416×416,and the detection speed is 64 frames per se-cond (FPS),which can satisfy the real-time detection and exhibit high precision and strong robustness for small target detection

Key words: Convolutional neural network, Improved YOLO v2, Ship target detection, Target detection

CLC Number: 

  • TP183
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