# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
## 2 Research on Edge Detection Method of Deep Learning Based on Improved HED  
### Edge Extraction Method Based on HED Network  
Deep learning has been a popular machine learning method in recent years. Many achievements have been made in image processing, search technology, data mining, machine translation, natural language processing, multimedia learning, machine learning, speech recognition and other related fields, among which image processing has become an important research field in deep learning. In recent years, the depth learning method has been constantly updated, and the edge extraction method based on depth learning can be used to extract the object appearance details, which is more suitable for describing the object. In the aspect of edge extraction, the HED algorithm is an end-to-end edge detection network. By multi-scale and multi-level feature extraction, it can predict the probability of the edge position and output the probability map of the edge response. It has the advantages of a fast operation speed and strong anti-noise ability, and can deal well with the complex texture information on the target and background. The edge extraction ability of natural images is far higher than that of traditional filtering methods such as Canny. Edge detection results of HED network on natural images are shown in Figure 1. The HED structure was obtained by modifying the 16 layer VGG. A side output was added after the final convolution layer of each stage. HED designed five side outputs, which were output from the conv1-2, conv2-2, conv3-3, conv4-3 and conv5-3 of the VGG16 network, respectively, and all the full connection layers of the VGG network were removed. The receptive fields of these side outputs were 5 \(\times\) 5, 14 \(\times\) 14, 40 \(\times\) 40, 92 \(\times\) 92 and 196 \(\times\) 196 respectively. The receptive field was constantly increasing, while the location information was constantly lost, and the semantic information was gradually enriched. The network deconvoluted these feature maps to make the size the same as the original image. Finally, the output results of each side output were fused through the weight value to obtain information results that included both the accurate location information of the low-level network and the rich semantic information of the high-level network, making the detection results more accurate. In the prediction phase, the output result of the side output could be directly used as the final result. The output results of all aide outputs could also be obtained, and then an average value was calculated as the final result, which further improved the accuracy. As shown in Table 1, side outputs 1-5 were the side output layers of the 1 st-5 th convolution layers, respectively. Fusion output was the output of the last layer, average 1-4 was the average output value of the 1 st-4 th convolutions, average 1-5, average 2-4, Average 2-5, and so on. The merged result was the combined result of the average values of the results of all layers. In the training process, the edge detection was actually a two-classification task for each pixel. Most of the pixels were non-edge, and only a few pixels were edge. The distribution of positive and negative samples was uneven, so ordinary cross-entropy loss could not be used. Here, pos_weight cross-entropy was used. The model output is shown in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **ODS** & **OIS** & **AP** \\ \hline
1 & Side-output 1 & 0.595 & 0.620 & 0.582 \\
2 & Side-output 2 & 0.697 & 0.715 & 0.673 \\
3 & Side-output 3 & 0.783 & 0.756 & 0.717 \\
4 & Side-output 4 & 0.740 & 0.759 & 0.672 \\
5 & Side-output 5 & 0.606 & 0.611 & 0.429 \\
6 & Fusion-output & 0.782 & 0.802 & 0.787 \\
7 & Average 1–4 & 0.760 & 0.784 & 0.800 \\
8 & Average 1–5 & 0.774 & 0.797 & 0.822 \\
9 & Average 2–4 & 0.766 & 0.788 & 0.798 \\
10 & Average 2–5 & 0.777 & 0.800 & 0.814 \\
11 & Merged result & 0.782 & 0.804 & 0.833 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: HED detection accuracy.