## 1 Introduction  
The inspection of power equipment plays a pivotal role in ensuring the secure and stable operation of the power grid. The use of deep learning in power systems such as wind power prediction [1], voltage stability assessment [2], power grid synthesis [3], solar power system control [4], power intelligent inspection of [5], etc. has grown rapidly in recent years. During the intelligent inspection in substation scenarios, inspection technologies based on UAV [6, 7] and robots [8] can utilize computer vision technology for the recognition of appearance defects in substation equipment (e.g., transformers, insulators, meters, etc. ), which can greatly alleviate the dangers to staff in high-risk environments such as substations. Meanwhile, the quality and efficiency of inspections in hazardous environments are improved. It requires the tracking and identification of power equipment targets in substations, and realizing real-time monitoring and analysis of equipment status. Therefore, the study of accurate and robust tracking algorithms is of great significance for substation inspection. However, for the power equipment targets in the inspection video, the complex background, uneven light distribution, large change amplitude, occlusion, and other factors of interference, resulting in power equipment segmentation tracking accuracy, has not yet reached the requirements of the production needs. To overcome the above problems, some trackers [9; 10] employ advanced template updating mechanisms to enhance their robustness. Recently, memory network has been effectively utilized in object tracking [11; 12; 13; 14; 15]. The tracking method [11] introduces a method that utilizes a combination of long-term and short-term memory to learn multiple adaptive correlation filters, enhancing the robustness of object tracking. A dynamic memory network [12; 13] is proposed that dynamically adjusts the template to accommodate variations in the target's appearance during the tracking process. In [14], a dual-memory selection (DMS) model is presented for reliable visual tracking, which effectively reduces tracking drift. By matching the reference frames with the query frame, STMTrack [15] transfers the reference frame information to the query frame, thereby directing the attention of the tracker to the most informative region of the current frame. In order to obtain the optimal match between the query frame and the reference frame in the memory network, VideoMatch [16] constructs templates for foreground and background based on the information from the initial frame, and utilizes the soft matching layer to generate similarity scores. Building upon VideoMatch, FEELVOS [17] and CFBI [18] incorporate information from the initial frame and the previous frame to generate templates. To maximize the utilization of historical frames for prediction purposes, STM [19] retrieves memory by employing non-local and dense memory matching techniques, and employs the retrieved memory to locate the target in the query. Extended from STM, episodic graph memory networks [20] are exploited to update memory segmentation models. NPMCA-net [21] utilizes a combination of non-local techniques and mask propagation to accurately localize foreground targets by comparing the pixels of reference and target frames. All of the above methods belong to surjective matching, also known as one-to-many matching, which only considers the query frame options without addressing reference frame options. This matching mechanism offers flexibility as there are no limitations on the matching process. Consequently, it enables effective handling of visually diverse frames but is vulnerable to potential background distractions. In order to prevent the problem, KMN [22] proposes a kernelized memory network, where the non-local matching between its query and memory is controlled by a Gaussian kernel that is generated by an argmax operation along the query frames, making the memory network more efficient for VOS.