# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 1 Introduction  
With the increasing number of high-voltage transmission lines, damage caused by birds to the power systems is increasing. Some birds build their nests on the transmission towers. During rain, the birds' nests act as conductors and trigger power line tripping. Similarly, in a dry environment, the branches of the bird's nest are prone to fire, which not only affects the normal power supply, but also poses a huge security risk. Thus, in order to ensure the safe and reliable operation of power grid systems, and reduce the adverse effects of bird activities on transmission lines and other equipment, the research problem of detecting and locating bird nests on transmission lines and poles is of great scientific importance and practical significance. Bird's nest detection on a high-voltage transmission line is a problem of image classification [1] and target-detection technology [2]. In literature, various methods for bird's nest detection on high-voltage transmission lines are presented. Most of these methods only consider the texture or color information of the bird's nest. This makes it impossible to locate the bird's nest accurately when the image's contrast is poor or the texture information is not rich. In [3], authors propose a method that uses texture, color, shape and nest's area to find if the image contains a bird's nest. However, the bird's nest does not possess distinct characteristics. For instance, objects such as branches and grass also have strong texture characteristics, and havethe same color. This forms the basis of interference during detection. Authors in [4] present a method that extracts histogram of oriented gradient (HOG) features of birds' nests in images and used support vector machine for classification. However, the proposed method has poor adaptability and is not suitable for bird nest detection in texture rich environments. In [5], authors use the color and texture features for detecting the birds' nests on high-voltage transmission lines. The proposed method first identifies the tower, then uses the color feature of the bird's nest to determine the area of interest. Finally, the algorithms eliminate the interference area using the gray-level co-occurrence matrix feature. However, some of the inspection images of high-voltage transmission lines have poor contrast. This makes it difficult to identify the bird's nest on the basis of color and analyze texture characteristics. Therefore, it is not possible to use color and texture characteristics to describe the samples. Similarly in [6], authors propose a bird's nest detection method for high-speed railway catenary system. The authors use the histogram feature of the direction of the burr and the length of the burr to characterize the nest structure. Authors then use support vector machine (SVM) to identify and classify the birds' nests. The proposed method has a high detection rate, however, the application scenario of bird's nest detection in this paper is high-voltage transmission lines. It is noticeable that the images usually include insulators, towers and other interferences. In addition, the thickness of transmission wires in the tower area is uneven and the direction is variable. Therefore, this method is not applicable to the image samples used in this paper. In [7], the authors use a fully convolutional network with a novel pyramid structure to generate face proposals efficiently. In addition, online and offline hard sample mining are combined to further enhance the ability of networks. However, the joint use of hard sample mining is not restricted. It has great randomness, which is not conducive to the specific optimization of the model. In [8], the authors propose a new adaptive hard sample mining algorithm for person re-identification task. Through comprehensive comparison of the hard level differences between training batches and the differences in demand for hard sample numbers, the model is optimized and the accuracy is improved.