# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 3 Datasets  
The datasets of foreign objects on power transmission lines utilized in this study were provided by the Electric Power Science Research Institute in Yunnan Branch of China Southern Power Grid Co., Ltd. The image data was collected using various drones, re sulting in significant variations in pixel dimensions, ranging from 640 \(\times\) 640, 856 \(\times\) 486, 1280 \(\times\) 720 to 2738 \(\times\) 2270. The sample images of foreign objects on power transmission lines were categorized into six classes: trash, twig, nest, kite, bird and balloon. Figure 1a represents trash, Figure 1b depicts twig, Figure 1c displays nest, Figure 1d exhibits kite, Figure 1e portrays bird, and Figure 1f illustrates balloon. It is essential to note that the images shown in Figure 1 are not original. Because the foreign objects are small, proportionally, in some original images, we extracted the partial images around foreign objects for a more explicit representation.  
Figure 1: Collection of Foreign Object Image Datasets for Transmission Lines. (**a**) trash. (**b**) twig. (**c**) nest. (**d**) kite. (**e**) bird. (**f**) balloon.  
Due to detailed latitude and longitude information in some sample images, we have covered this information with black blocks according to data security requirements.  
The overall images for each category are presented in Figure 1. It is evident from Figure 1 that, due to varying shooting angles, the images of foreign objects on power transmission lines exhibit challenges such as occlusions, diverse categories of foreign objects, considerable variations in object scales, and complex backgrounds. These factors significantly augment the difficulty in detecting foreign objects on power transmission lines.  
Given the infrequent occurrence of foreign objects on power transmission lines, the samples obtained are limited. In the original dataset, the respective counts of actual labels for trash, twig, nest, kite, bird and balloon are 667, 64, 2508, 240, 1244, and 356, totaling 3774 images in the dataset. These samples are insufficient for training neural networks, necessitating dataset augmentation.  
As depicted in Figure 2, below, we augmented the datasets through rotations, variations in brightness, salt-and-pepper noise, and occlusions, resulting in an expanded dataset comprising 11,323 images. Following augmentation, the counts of actual labels for trash, twig, nest, kite, bird, and balloon are 2024, 1299, 4017, 2133, 2489, and 1811, respectively. The quantities per category are detailed in Table 1.  
In subsequent experiments, to assess the effectiveness of the improved model, we randomly divided both the original and augmented dataset into training set, validation set, and test sets in a 6:2:2 ratio, for experimentation with the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Trash** & **Twig** & **Nest** & **Kite** & **Bird** & **Balloon** \\ \hline Original & 667 & 64 & 2508 & 240 & 1244 & 356 \\ \hline Data Augmentation & 2024 & 1299 & 4017 & 2133 & 2489 & 1811 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total Amount of Each Type of Data.  
Figure 2: Example of data augmentation. (**a**) original. (**b**) rotations. (**c**) variations in brightness. (**d**) salt-and-pepper noise. (**e**) occlusions.