# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 1 Introduction
However, due to the lack of pertinence, this method is not effective in detecting small-sized objects. In recent years, breakthroughs have been made in the field of machine learning [9]. In the field of machine learning, deep learning has caused an unprecedented and tremendous impact [10]. Deep learning uses neural networks to solve linear inseparable problems. Deep learning uses large amounts of data and learns automatically without manual intervention. Deep learning is widely used in target recognition [11] and multi-target detection [12]. Multiple feature maps are generated in the neural networks, each of which corresponds to many neurons. The features are extracted using a convolution filter [13]. The convolution operation [14] not only enhances the original features of signals but also reduce noise embedded in the imagery. For filters with the same step, the larger the image, the greater the number of neurons and the larger the number of weight parameters that need to be trained. Consequently, the training speed is low. Thus, the sample size is adjusted several times during the training process in order to improve the training efficiency and reduce the training overhead. We use existing algorithms for object detection, and the accuracy is low. The main problem is that, during the model's training process, thousands of candidate regions may be generated from an input image. However, only a small number of these candidates contain the object of interest. This leads to a serious imbalance of the proportion between positive and negative samples, thus leading to class imbalance problem. Class imbalance leads the overall learning towards the useless, easily divided counterexample samples. Thus, resulting in invalid learning, i.e., only the background without objects can be distinguished, but the specific objects cannot be distinguished. Moreover, if the number of negative samples that are easy to classify are too large as compared to positive samples, it will have a negative impact on detection model optimization. In response to this problem, we propose a new automatic detection framework--region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). The main contributions of this work are as follows:  
1. We propose a new hard sample mining algorithm for object detection task. Through the comprehensive analysis of the datasets, the size characteristics of the detection objects are determined, and the limited conditions are given. The ROI mining method can focus better on the difficult-to-classify small-scale objects and optimize the model in a targeted manner. 2. According to the characteristics of the annotation boxes of the datasets, we obtain adaptive prior anchors by using k-means clustering. This method reduces the convergence time of the model and improves the accuracy of the coordinate boxes generated. 3. We combine the focal loss function in the model to solve the problem of class imbalance during the training process. This can improve the detection accuracy of the model. The results presented in this work show that the proposed method achieves better results as compared to other methods proposed in the literature.