## 1 Introduction  
Overhead transmission lines are the most commonly used method for power delivery, whose safety and stability are the premise of normal power supply, thus the inspection and maintenance of transmission lines are of vital importance [1, 2]. With the increase in power demands, the geographical span of transmission lines is also gradually increasing, which may pass through densely populated urban areas and construction sites. Due to the diverse distribution areas of transmission lines and the influences of human and bird activities, etc., the transmission corridors may be invaded by foreign objects such as kites, balloons, plastic and nests, and results in short-circuit and ground discharges [3, 4]. Traditional transmission line inspection is usually carried out by manual inspection. Due to the complex terrain environment along the transmission corridors and the possible artificial misjudgment, manual inspection is unable to meet the practical requirements [5]. In recent years, unmanned aerial vehicle (UAV) [6, 7] and monitoring equipment installed on transmission towers [8] have been widely used to acquire inspection images. In addition, the classification, recognition and object detection of the massive inspection images using computer vision algorithms have been widely studied. At present, the object detection methods are generally divided into two main categories. One is traditional object detection that combines image processing, feature extraction and object identification by classifiers, the other is the detection method based on deep learning. Traditional detection method adopts manual feature extraction and machine learning classifiers, the input images often should be preprocessed to deal with the effects of complex backgrounds. [PERSON] et al. [9] proposed a method based on GMM and K-means to cluster and detect foreign objects. [PERSON] et al. [10] proposed a method for rotation invariant insulator detection that extracted the sliding window based on local directional pattern (LDP) feature from the image and used the support vector machines (SVM) for classification of those sliding windows. [PERSON] et al. [11] put forward a detection method for bird's nests on transmission towers based on cascade classifier and combination features, whereas the images were described by the proportion of white area (PWA), ratio of white pixels (RWP), projection feature (PF) and improved burr feature (IBF), and recognized by the cascade classifier. Traditional detection methods require a series of image preprocessing and manual feature extraction to detect the targets [12], the process is complicated, with poor universality and low accuracy in practical complex engineering environment. In recent years, deep learning algorithms have been widely used in transmission line defects detection and perform well in accuracy [13]. [PERSON] et al. [14] proposed a defect recognition method for key components of transmission lines based on improved Faster R-CNN, which used the concept-ResNetv2 feature extraction network to achieve the network structure adjustment and parameter optimization. [PERSON] et al. [15] proposed a foreign object detection model based on Faster R-CNN using data augmentation method by rotation, adjusting brightness and color saturation, adding Gaussian noise to expand the images, the recognition accuracy of the improved model can reach 93.09%. [PERSON] et al. [16] proposed an efficient foreign objects detection network for power substation (FODN4 PS), which consists of a moving object region extraction network and a classification network. The experiment proved that this model has a higher accuracy than Fast R-CNN and Mask R-CNN. [PERSON] et al. [17] proposed a hybrid knowledge region-based convolutional neural network (HK R-CNN) to detect aerial images, and the results show that it has higher performance than Faster R-CNN, which can accurately detect the multiple fittings on transmission lines. [PERSON] et al.