An improved plasma boundary detection based on an optimized U-Net deep learning network for Tokamak devices

Abstract Tokamak is a typical nuclear fusion device, where effective control of the plasma boundary contributes to its long-pulse stable operation. Nowadays, CCD camera diagnostic systems are widely used in most fusion devices. By detecting the CCD images of plasma discharge, the plasma boundary can be obtained. The U-Net deep learning network, with its excellent feature extraction and boundary perception capabilities, possesses excellent image detection characteristics and can be applied in boundary detection. However, automated and accurate detection of plasma boundaries from these images remains challenging due to their inherent complex characteristics, such as low contrast, blurry edges, nonuniform brightness distribution, and high inter-class similarity. To address the challenge of automated, high-precision plasma boundary detection in Tokamak devices, this paper develops a novel diagnostic method based on an optimized deep learning network. Firstly, batch normalization (BN) layers are introduced between each convolution operation and activation function in the encoder-decoder, effectively mitigating internal covariate shift during small-sample training and significantly improving training speed and gradient stability. Furthermore, by embedding the convolutional block attention module (CBAM) before each downsampling, the U-Net network is enabled to adaptively enhance the response of discriminative feature channels and key spatial regions, effectively distinguishing visually similar areas such as the plasma core, edge, and background. Additionally, a hybrid loss function combining cross-entropy and Dice loss is designed to enhance boundary sensitivity while maintaining smooth and stable training, thereby alleviating model bias caused by class imbalance. To verify the effectiveness of the detection algorithm, plasma discharge images through the CCD camera on the Thailand Tokamak-1 (TT-1) device are obtained, and a specialized dataset for plasma image detection is constructed. Experimental results demonstrate that the optimized model achieves a final mIoU of 92.42%, mPrecision of 96.67%, and mRecall of 95.24%, significantly outperforming both the original U-Net network and Canny edge detection. The proposed algorithm not only reduces misjudgment and boundary discontinuities but also exhibits stronger robustness and generalization capability, confirming its suitability for accurate plasma boundary detection.