The precise plasma boundary gap identification at the midplane is a prerequisite for achieving controlled plasma positioning and holds a significant importance for the stable operation of tokamak devices. This study proposes a plasma boundary gap at the midplane recognition algorithm based on visual endoscopy diagnostic. The model is an end-to-end one that uses a convolutional neural network that does not require manual data labeling. The model performance is improved by experimentally comparing different convolutional layers and input image sizes. The model is validated using a testing dataset comprising 400 plasma discharge moments. The model has average errors of 3.7 and 4 mm for gap-in and -out, respectively, when compared to those obtained by equilibrium fitting. The proposed approach offers a convenient and effective means of obtaining the boundary gap value and is particularly suited for future fusion experimental devices, such as BEST and ITER tokamak.

The presence of a bright spot structure in the divertor region during the discharge process, indicative of localized overheating, has been observed through multi-band and high-speed endoscope diagnostic on the Experimental Advanced Superconducting Tokamak (EAST). This localized deposition of hyperthermal heat flux can lead to erosion and melting of the target plate material, thereby posing a significant risk to the safe operation of the device. Moreover, it may introduce impurities into the main plasma, negatively impacting plasma performance. Therefore, real-time monitoring of the divertor and rapid identification of localized overheating regions during experiments are crucial. In this context, this paper proposes an improved DeepLabv3+-based highlight structure image-segmentation algorithm, which uses minimum value, image difference method, and Prewitt operator for dataset preprocessing. In order to realize the rapid identification of local overheated regions, this paper introduces the application of the improved DeepLabv3+ neural network algorithm based on MobileNetV2 as the backbone network in the bright spot structure segmentation task for the first time. The results show that the algorithm achieves a 65.36% average crosslinking rate (mIoU), 78.75% accuracy, 0.78 s per-iteration processing time, and 22.4 MB parameter size. This provides substantial advantages in terms of reduced computing and memory resources and real-time detection performance. Ultimately, the method proposed in this paper enables the rapid identification of the bright spot structure in the localized overheating region of the divertor on the EAST; it identifies areas of overheating and prevents damage to the divertor or other critical components due to overheating, ensuring safe operation of the device.