深度卷积神经网络在辐射环境下核废料检测中的应用

针对辐射环境下核废料检测准确率低的问题，提出一种基于深度卷积神经网络的辐射环境下核废料检测算法Dense-Dilated-YOLO V3。实验结果表明，Dense-Dilated-YOLO V3在不增加参数的情况下扩大了网络感受野，也有效避免图像信息的损失，同时能够在核辐射环境下提取到更多的目标细节特征，对辐射环境下目标检测的准确率可达93.29%，比原算法提高5.53%，召回率可达91.73%，提高了8.28%，有效解决了复杂辐射环境下核废料检测准确率低的问题，为辐射环境下核废料检测提供了新的途径。

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

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.