## 5 Discussion  
The methods for foreign objects detection on transmission lines can be classified into two categories. One is the traditional detection models, such as the method combining GMM and K-means clustering in [9] and that based on combination features and cascade classifier in [11], etc. These methods should extract artificially designed features from the images, which were greatly affected by image processing results and the complex backgrounds in the images. Therefore, the traditional detection models are usually not suitable for practical engineering applications. The proposed method in this paper adopts the Otus binarization threshold segmentation and morphology processing to extract the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Baseline Network**} & \multicolumn{4}{c}{**Indexes/\%**} \\ \hline
**N1** & **N2** & **N3** & **N4** & **N5** & **Accuracy** & \(macro\_P\) & \(macro\_R\) & \(macro\_F_{1}\)** \\ \hline \(\surd\) & \(\surd\) & & & 93.81 & 92.52 & 94.31 & 93.41 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 95.88 & 96.33 & 95.19 & 95.76 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 92.78 & 95.40 & 90.43 & 92.85 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 93.81 & 95.02 & 92.39 & 93.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results under different fusion schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{4}{c}{**Indexes/\%**} \\ \cline{2-5}  & **Accuracy** & \(macro\_P\) & \(macro\_R\) & \(macro\_F_{1}\) \\ \hline DenseNet-201 & 92.78 & 92.22 & 91.21 & 91.71 \\ GoogLeNet & 94.85 & 94.83 & 93.79 & 94.31 \\ AlexNet & 90.72 & 91.50 & 88.97 & 90.22 \\ ResNet-101 & 91.75 & 92.33 & 89.69 & 90.99 \\ EfficientNet-B0 & 93.81 & 94.81 & 92.41 & 93.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance indexes of different network models.  
target region in the images, thus eliminating the influence of the complex backgrounds on the image feature extraction. Meanwhile, the CNNs are used to extract the features automatically, which is able to overcome the shortcomings of manual feature extraction.  
The other is the detection models based on deep learning algorithms, such as SSD [13], Faster R-CNN [15], improved YOLOv3 [19] and YOLOv4 [21], etc. These methods require high-performance equipment to train the detection models, which are usually time-consuming and need a large number of training samples. The proposed method uses five pre-trained networks to extract the image features, and adopts the random forest to establish a machine learning model, thus achieving classification of foreign objects. This strategy is useful to overcome the disadvantages of object detection algorithms based on deep learnings.  
The detection accuracy was taken as an evaluation index to compare the proposed method with other foreign object detection methods in previous studies. The detection accuracies of these methods are summarized in Table 7. It can be seen that the proposed method has a higher accuracy compared with most of the published foreign object detection methods, only a little bit lower than that based on combination features and cascade classifier. The latter should extract the features manually, and only verified by bird's nest detection cases, whereas the proposed method can achieve satisfied accuracy with automatic feature extraction. The comparison results verify the superiority of the proposed method based on multi-network feature fusion and random forest, and it is useful for foreign objects detection on transmission lines.