## 2 Improved YOLO V3 Network Model and Model Training
### Improved YOLO Network Structure  
To realize real-time detection of precision parts, we use Tiny-YOLO with a simple structure and low computational complexity as the basic network of YOLO V3. Tiny-YOLO comprises 19 convolutional layers and 4 pooling layers, which alternately form a feedforward network, and easily lead to information loss layer by layer. This network fails to utilize multilayer feature information and reduces detection accuracy. To realize multilayer feature multiplexing and fusion, and to avoid the computational complexity caused by the new structure, we utilize the concept of a multilevel feature map fusion network proposed by [PERSON] et al. [25], although only in the low-resolution of the YOLO V3 network feature map. The Tiny-YOLO's 14 th layer (resolution 1313) is replaced with dense modules (dotted line in Figure 4) by deeply embedding the dense modules to build a YOLO-dense network with dense connections (Table 1). A 14-volume layer can receive the multilayer convolution features of densely connected block outputs for feature multiplexing and fusion. The simplified network structure consists of seven convolutional layers and six pooling layers, as well as a feature reorganization layer and two convolutional layers for generating candidate frames and classifications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Operation Type** & **Kernel Size** & **Stride** & **Input** & **Output** \\ \hline
1 & Convolution layer & \(3\times 3\) & 1 & \(416\times 416\times 3\) & \(416\times 416\times 16\) \\
2 & Pooling layer & \(2\times 2\) & 2 & \(416\times 416\times 16\) & \(208\times 208\times 16\) \\
3 & Convolution layer & \(3\times 3\) & 1 & \(208\times 208\times 16\) & \(208\times 208\times 32\) \\
4 & Pooling layer & \(2\times 2\) & 2 & \(208\times 208\times 32\) & \(104\times 104\times 32\) \\
5 & Convolution layer & \(3\times 3\) & 1 & \(104\times 104\times 32\) & \(104\times 104\times 64\) \\
6 & Pooling layer & \(2\times 2\) & 2 & \(104\times 104\times 64\) & \(52\times 52\times 64\) \\
7 & Convolution layer & \(3\times 3\) & 1 & \(52\times 52\times 64\) & \(52\times 52\times 128\) \\
8 & Pooling layer & \(2\times 2\) & 2 & \(52\times 52\times 128\) & \(26\times 26\times 128\) \\
9 & Convolution layer & \(3\times 3\) & 1 & \(26\times 26\times 128\) & \(26\times 26\times 256\) \\
10 & Pooling layer & \(2\times 2\) & 2 & \(26\times 26\times 256\) & \(13\times 13\times 256\) \\
11 & Convolution layer & \(3\times 3\) & 1 & \(13\times 13\times 256\) & \(13\times 13\times 512\) \\
12 & Pooling layer & \(2\times 2\) & 1 & \(13\times 13\times 512\) & \(13\times 13\times 512\) \\
13 & Convolution layer & \(3\times 3\) & 1 & \(13\times 13\times 512\) & \(13\times 13\times 1024\) \\
14 & Recombination layer & - & - & 9 output layers + 13 & \(13\times 13\times 2048\) \\
15 & Convolution layer & \(3\times 3\) & 1 & \(13\times 13\times 2048\) & \(13\times 13\times 1024\) \\
16 & Convolution layer & \(1\times 1\) & 1 & \(13\times 13\times 1024\) & \(13\times 13\times 30\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the identification model network of precision parts.  
Figure 4: Structure of identification model network of precision parts.  
Figure 3: Removal of invalid candidate boxes using the non-maximum suppression algorithm.