## 3 Materials and Methods  
### First-Frame Localization Model Based on Improved RFBNet  
#### 3.1.1 Network Infrastructure  
The objective of this paper is to optimize the RFBNet-based first-frame localization model through the introduction of techniques derived from ShuffleNet V2 [17] and GIoU [18]. This is to enhance the operational efficiency of the first-frame localization model and the accuracy of the bounding box of the first-frame localization on the industrial control machine. The network structure of the first-frame localization model, which has been enhanced through the application of the aforementioned techniques, is illustrated in Figure 2.  
The ShuffleNet V2 network offers several advantages, including a reduced number of network parameters, low computational complexity, rapid operation speed, and high recognition accuracy. Accordingly, the lightweight ShuffleNet V2 network is employed instead of the original VGG16 backbone network, thereby reducing the computational complexity through the introduction of a channel-splitting strategy. This strategy divides the channels of the perforated robotic arm feature map into two parts: direct output and convolutional processing. Following the rearrangement of the convolutionally processed channels, distinct groups of features are combined, thereby enhancing the model's capacity for characterization. Furthermore, point-by-point convolution and depth-separable convolution are employed in ShuffleNet V2 to reduce the number of parameters and computations. Point-by-point convolution can be divided into two categories. The first is  
Figure 2: Network structure of initial frame positioning model based on improved RFBNet. Schemes follow the same formatting. employed to augment the number of channels in the feature map, thereby enabling the model to capture a more nuanced array of information. The second is utilized to reduce the number of channels to the original size, which effectively compresses the information and reduces the number of parameters. Following point-by-point convolution, each input channel is initially convolved channel-by-channel using depth-separable convolution (DWConv). Subsequently, the channel information is mixed by point-by-point convolution. This structure enables ShuffleNet V2 to markedly reduce computational resource consumption while maintaining high performance, thus adapting to the resource constraints of the industrial controllers of the edge intelligent terminals. The structure diagram is shown in Figure 3.  
To enhance the efficacy of the RFBNet first-frame localization model, we optimized the backbone network with four stages, namely, Conv1, Stage 2, Stage 3, and Stage 4, of the ShuffleNet V2 network. In light of the potential impact of the maximum pooling layer on the receptive domain of the feature map, we have elected to remove the initial maximum pooling layer by the specifications of ShuffleNet V2. In the optimized backbone network, the processing flow of the perforated robotic arm image is as follows: firstly, the Conv1 layer applies a 3 \(\times\) 3 convolution kernel to the input perforated robotic arm image with a step size of 2, thus effectively reducing the size of the input image to half of the original. Subsequently, the images from Stages 2, 3, and 4 are subjected to feature extraction and transformation through the use of multiple ShuffleNet V2 blocks. In particular, Stage 2 comprises one ShuffleNet V2 block (b) and three ShuffleNet V2 blocks (a). The combination of different types of blocks enables the model to capture both local details and global information about the image. Similarly, Stage 3 contains one ShuffleNet V2 block (b) and seven ShuffleNet V2 blocks (a) for further feature extraction and enhancement, while Stage 4 contains one ShuffleNet V2 block (b) and three ShuffleNet V2 blocks (a) to provide a rich feature representation for the final first frame localization.