There is interference caused by complex working conditions such as strong arc light, smoke and dust, and extreme thermal radiation during the TIG welding, and interference for visual feature extraction of the molten pool due to the reflection characteristic instability of the molten pool region caused by the dynamic flow of liquid metal. In view of this, this study proposes an improvement method for molten pool measurement, which includes a lightweight network molten pool segmentation method based on the attention mechanism and multi-scale feature fusion, and an image processing method of closing operation, connected region labeling, and the minimum bounding rectangle based on the segmentation results. The results show that the improved network has enhanced performance on the self-built molten pool segmentation datasets. The mean intersection over union (MIoU) reaches 95.44%, the mean pixel accuracy (mPA) is 98.27%, and the inference time for a single frame is only 11.30 ms. Additionally, the length, width, and area of the segmented molten pool are effectively extracted.