Edge intelligence models commonly used to identify abnormal targets in transmission lines are easily affected by complex weather changes

triggering significant declines in detection accuracy and even detection failure for abnormal targets

thus compromising safe and stable operation of transmission lines. An asymptotic domain adaptive detection method for abnormal targets in transmission lines under complex weather conditions is thus proposed in this paper. An approximate domain closer to the target domain is first constructed

utilizing an image style transfer method; this is followed by a proposed multilayer domain transfer method that integrates a spatial attention mechanism that causes the model to pay more attention to feature information of abnormal targets

further shortening the distance between approximate domain and target domain. Finally

a SAM strategy is introduced into the domain adaptive task

creating a detection model for abnormal targets of transmission lines that is driven to access additional effective domain invariant information to make the landscape of task loss function smoother. In this study

detailed experiments are thus carried out based on an abnormal target dataset from various transmission lines. Experimental results show that the method improves accuracy of abnormal target recognition under complex weather conditions to 91.97%

offering recognition accuracy obviously superior to a single-step domain adaptive SOTA method or a variety of SOTA-based edge detection models for abnormal targets in transmission lines

and thus displaying good application prospects.