###### Abstract  
As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird's nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottekNect structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves \(mAP50\), Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. transmission line; foreign object detection; RevCol algorithm; detection head; loss function +
Footnote †: journal:  
detection method for broken strands and foreign object defects in transmission lines based on line structure perception, which uses gradient operators to extract line objects and identifies significant parallel wire groups in transmission line structures through parallelism calculations. Wire breakage and foreign object defects can be identified by calculating the width change and grayscale similarity of segmented wires. [PERSON] et al. [12] discusses the use of [PERSON]'s linear transform for transmission tower line extraction, combined with convolution operations in small areas of the transmission line. However, the detection efficiency of this method is impacted by complex backgrounds and noise, resulting in lower accuracy. [PERSON] et al. [13] adopts the frame difference method to label foreign objects and uses key-frame extraction and foreign object feature point tracking to achieve the purpose of foreign object detection in transmission lines. [PERSON] et al. [14] used the Linear Segment Detection algorithm to extract power lines from images and designed a recognition algorithm to detect foreign objects based on foreign object features. Traditional foreign object detection methods can detect foreign objects to a certain extent; however, these methods exhibit low detection accuracy, focus on single-target detection, lack generalization ability, are not scalable, and suffer from false detection leaks. With the development of deep learning and neural networks, the integration of image classification and detection with computer vision technology [15] has made further contributions to the development of the smart grid field [16; 17; 18]. Recent advancements in computer image classification and detection technologies have significantly reduced manual operations and the likelihood of misjudgments in identifying foreign objects on transmission tower lines. These technologies not only expedite the screening process but also ensure the safe and efficient functioning of power systems. Key developments in this area, as reported in the various literature, include the following. [PERSON] et al.