###### Abstract
[19] compared and analyzed DPM (Deformable Part Model), Faster R-CNN, and SSD methods using actual datasets of transmission line foreign objects and verified the feasibility of deep learning-based recognition methods in the real-time detection of transmission line foreign objects. [PERSON] et al. [20] used the TensorBoard module in the TensorFlow framework to design the deep convolutional neural network model structure and optimize the model parameters after grayscaling and denoising the foreign object dataset. [PERSON] et al. [21] used the K-means algorithm to cluster the size of foreign object images to set the size of anchor frames, and enhancements include an upsampling module, depth-separable convolution, and improved loss functions for better classification and detection. [PERSON] et al. [22] optimized the candidate frame and designed an end-to-end joint training approach called TLFOD Net to improve the model training performance. [PERSON] et al. [23] improved three aspects of the YOLOv3 algorithm, including the width and height loss function of the prediction frame, the prediction category imbalance loss function, and the neural network structure, to improve the foreign object recognition effect. [PERSON] et al. [24] improved the feature pyramid pooling module based on YOLOv4 and optimized the loss function to improve the algorithm performance. [PERSON] et al. [25] used k-means clustering to generate anchor boxes and then improved the spatial pyramid pooling (SPPF) module and activation function, and this approach significantly enhanced the model performance. [PERSON] et al. [26] used GhostNet module to replace the backbone feature extraction network of YOLOv4, improved the PANet module, and replaced the ordinary convolutional block with a depth-separable convolutional module, which improved the detection speed and reduced the model parameters, although the detection accuracy was degraded. [PERSON] et al. [27] used Otsu to extract the target region of interest, DenseNet201 to extract the depth features of the target region, and ECOC-SVM for training and testing to improve the detection accuracy. Current object detection algorithms often compromise detection accuracy by compressing or discarding features during extraction. To address this, our paper introduces a novel algorithm specifically tailored for detecting foreign objects on transmission tower lines. This algorithm diverges from the norm by improving upon the conventional target detection model. Key innovations include: 1. Utilizing RevCol as the primary backbone network to ensure that the total information remains unchanged during the feature decouplingprocess. 2. Integrating an enhanced C2f module behind the backbone network to enable the model to capture both low-level detail information and high-level semantic information and replacing the standard C2f in the BottleNeck with DySnakeConv to better capture relevant features. 3. Employing a decoupled detection head, which separates classification and regression tasks, and adding an auxiliary training head. This head is actively involved during the training phase and removed during final inference, thereby refining the model's detection accuracy. 4. The MPDIoU method is incorporated to overcome challenges in bounding box regression, especially when predicted and actual boxes share aspect ratios but differ in dimension. To validate the effectiveness of our proposed model, we conducted comparative analyses with other mainstream target detection models. The results affirm the superiority of our approach in accurately detecting foreign objects on transmission tower lines.