Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 118-121.doi: 10.11896/jsjkx.200700122

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Tiny YOLOv3 Target Detection Algorithm Based on Region Activation Strategy

YU Han-qing, YANG Zhen, YIN Zhi-jian   

  1. School of Communication and Electronics,Jiangxi Science and Technology Normal University,Nanchang 330000,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:YU Han-qing,born in 1996,postgra-duate.Her main research interests include object detection and so on.
    YANG Zhen,born in 1985,Ph.D.His main research interests include pattern recognition and intelligent systems,machine learning and image processing.
  • Supported by:
    National Natural Science Foundation of China(61866016),Youth Top-notch Project of Jiangxi Science and Technology Normal University(2018QNBJRC002),General Project of Jiangxi Provincial Department of Education (GJJ190587),General Project of Natural Science Foundation of Jiangxi Province(20202BABL202014) and Oracle Information Processing Ministry of Education Key Laboratory Open Project Funding Project(OIP2019E008).

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Abstract: Aiming at the problem of low detection accuracy of Tiny YOLOv3 model,a method to introduce segmentation information into deep convolutional neural network structure is proposed.During the model training,the real position information of the target is added to the network layer,and these target areas are manually activated.The size of the excitation gradually decreases as the training proceeds until it drops to zero.The test results show that on the VOC2007 data set,the average accuracy of the improved Tiny YOLOv3 model is increased to 58.9%,and the detection speed is consistent with the original model to meet the needs of real-time detection.

Key words: Deep convolutional neural network, Location information, Segmentation information, Tiny YOLOv3

CLC Number: 

  • TP391
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