To reduce the impact of foggy days on transmission line inspection images, a foggy day transmission line inspection image de-fogging method, Diff-EaT, is proposed for the current mainstream de-fogging algorithms that have high computational costs, poor detection performance after image de-fogging, and difficult to deploy. The method adopts a fusion Transformer's diffusion model structure, and to reduce the computational complexity of the multi-head self-attention in the feature extraction module in the ViT, multi-head external attention is used instead of multi-head self-attention to reduce the computational load and enhance feature learning. Meanwhile, a mixed-scale gated feed-forward network is designed to integrate a pick-and-pass mechanism after the depth-separable convolution of input features to improve local information capture. Tested on synthetic and real datasets, quantitative and quantitative metrics prove their effectiveness with clearer details of recovered images. In the defogging detection system, the real inspection images are defogged and then detected using YOLOv7, mAP@0.5, recall rate, and checking accuracy rate are improved by 6.92%, 9.58%, and 4.11%, respectively, and this paper's method effectively improves the detection confidence after defogging. Defogging detection systems can be applied in real-world scenarios. Also in ablation experiments to demonstrate the effectiveness of its improvements.