Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 226-232.doi: 10.11896/jsjkx.210100058

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Camouflaged Object Detection Based on Improved YOLO v5 Algorithm

WANG Yang1, CAO Tie-yong1, YANG Ji-bin1, ZHENG Yun-fei1,2,3, FANG Zheng1, DENG Xiao-tong1, WU Jing-wei1, LIN Jia4   

  1. 1 Insitute of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 The Army Artillery and Defense Academy of PLA,Nanjing 211100,China
    3 The Key Laboratory of Polarization Imaging Detection Technology,Hefei 230031,China
    4 Shandong Military Region,Ji'nan 250000,China
  • Received:2021-01-20 Revised:2021-05-08 Online:2021-10-15 Published:2021-10-18
  • About author:WANG Yang,born in 1996,postgra-duate.His main research interests include object detection and image processing.
    CAO Tie-yong,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include computer vision and image processing.
  • Supported by:
    National Science Fund for Distinguished Young Scholars of China(61801512),National Natural Science Foundation of China(62071484) and Natural Science Foundation of Jiangsu Province(BK20180080).

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Abstract: Since the camouflage object is highly similar to the surrounding environment with a rather small size,the general detection algorithm is not fully applicable to the camouflaged object detection task,which makes the detection of camouflaged object more challenging than the general detection task.In order to solve this problem,the existing methods are analyzed in this paper and a detection algorithm for camouflage object is proposed based on the YOLO v5 algorithm.A new feature extraction network combined with attention mechanism is designed to highlight the feature information of the camouflage target.The original path aggregation network is improved so that the high,middle and lowly level feature map information is fully fused.The semantic information of the target is strengthened by nonlinear pool module,and the detection feature map size is increased to improve the detection recall rate of the small size target.On a public camouflage target dataset,the proposed algorithm is tested with 7 algorithms.The mAP of the proposed algorithm is 4.4% higher than that of the original algorithm,while the recall rate has improved 2.8%,which verifies the effectiveness of the algorithm for camouflaged object detection and the great advantage in accuracy compared with other algorithms.

Key words: Aggregation network, Attention mechanism, Camouflaged object, Object detection, YOLO

CLC Number: 

  • TP751
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