Application of deep convolutional neural network in detection of nuclear waste in radiation environment

Xiang Wei; Shi Jinfang; Liu Guihua; Xu Feng

doi:10.11884/HPLPB201931.190220

Volume 31 Issue 11

Oct. 2019

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Article Navigation > High Power Laser and Particle Beams > 2019 > 31(11): 116001

Xiang Wei, Shi Jinfang, Liu Guihua, et al. Application of deep convolutional neural network in detection of nuclear waste in radiation environment[J]. High Power Laser and Particle Beams, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220

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Xiang Wei, Shi Jinfang, Liu Guihua, et al. Application of deep convolutional neural network in detection of nuclear waste in radiation environment[J]. High Power Laser and Particle Beams, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220

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Application of deep convolutional neural network in detection of nuclear waste in radiation environment

doi: 10.11884/HPLPB201931.190220

Xiang Wei^{1, 2
,},
Shi Jinfang^{1, 2
,
,},
Liu Guihua²,
Xu Feng²

1.
Key Laboratory of Technology for Manufacturing Process, Southwest University of Science & Technology, Mianyang 621010, China
2.
Special Environment Robotics Laboratory of Sichuan Province, Southwest University of Science & Technology, Mianyang 621010, China

Received Date: 2019-06-17
Rev Recd Date: 2019-09-04
Publish Date: 2019-11-15

Abstract

Abstract

Aiming at the low accuracy of nuclear waste detection under radiation environment, this paper proposes a nuclear waste detection algorithm named Dense-Dilated-YOLO V3 based on deep learning convolution neural network. The experimental results show that Dense-Dilated-YOLO V3 increases the network receptive field without increasing the parameters, effectively avoids the loss of image information, extracts more detailed features of the target in the radiation environment, and accurately detects the target under radiation environment. The rate reached 93.29%, which was 5.53% higher than the original algorithm, and the recall rate reached 91.73%, with an increase of 8.28%. It solved the problem of low accuracy of nuclear waste detection under complex radiation environment, and has better detection effect. It provides a new approach for nuclear waste detection.
- deep learning,
- convolutional neural network,
- YOLO V3,
- nuclear waste,
- target detection

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References(17)

References

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