# Research on the Strawberry Recognition Algorithm Based on Deep Learning
## 2 Materials and Methods
### Introduction of Faster R-CNN Network Structure and Optimization  
Faster R-CNN [24; 25; 26; 27] is the latest network structure of the R-CNN two-stage object detection series, which has achieved significant improvements in detection speed and precision. Compared to single-stage detection, its algorithm is more complex and slower, but its detection precision will be higher. The main steps of Faster R-CNN proposed in the article include image feature extraction, RPN (Region Proposal Network), ROI (Region of Interest) pooling, classification and regression. Firstly, the input image size \(\text{P}\times\text{Q}\) is adjusted to fix the short edge to 600. Then, the backbone feature extraction network of Faster R-CNN uses transfer learning lightweight networks such as (ResNet50, Vgg16, etc.) to obtain a shared feature layer (Feature Map). Afterward, the shared feature layer passes the data into the RPN and ROI Pooling layers, incorporating NMS, an automatic learning mechanism for generating high-quality region proposals, avoiding many redundant regions in traditional methods and significantly improving detection efficiency. Finally, in the classification and bounding box regression stages, using the previously extracted features, Faster R-CNN performs object classification and fine adjustment of bounding box positions through fully connected layers. This stage not only improves the precision of object detection but also enables the model to make more accurate predictions of object positions. Overall, Faster R-CNN achieved optimization of precision and speed in object detection by integrating region proposal networks into an end-to-end learning network, demonstrating excellent performance (as shown in Figure 4(a)). Figure 4(b) is the MRS Faster R-CNN structural framework. It can be seen in the structural block diagram that the data set is expanded and improved. The ResNet50 backbone network is used, and the Soft-NMS module is added to the RPN structure. Figure 4: Image of data augmentation process. 2.3.1. ResNet50 Backbone Feature Extraction Network  
ResNet50 [28] (pp. 770-778) was invented by [PERSON], [PERSON], [PERSON], and [PERSON] It has achieved ImageNet classification, ImageNet detection, ImageNet localization, COCO detection, COCO in the 2015 ILSVRC and first place in COCO segmentation competitions and has since shot to fame. Neural networks are not necessarily better with more layers. ResNet's structural highlights can solve the problem of traditional deep networks causing gradient vanishing and exploding with increasing depth, leading to a rapid increase in error rate. It can effectively achieve a decrease in error rate with increasing depth, effectively improving the detection precision and convergence speed of high-depth networks. The residual module can effectively learn identity mapping, which is achieved through \"shortcut connections\". If the identity mapping is the optimal model, simply set the weight in the weight layer to 0 (Figure 6). Assuming the input is X, the relationship between layers is F(X). The output of the residual block is y (Equation (6)), and what the model wants to learn is the residual of F(X, \(\{W_{i}\}\)) instead of y, the equation changes to Equation (7). If the number of channels of X is not the same as the number of channels of F(X), it cannot be directly superimposed, and it needs to go through a 1 \(\times\) 1 convolutional kernel to raise the dimension (Figure 7). In a network with the same number of layers, it can directly connect input data to the output end, thereby reducing a large number of parameters and effectively solving the degradation problem of deep neural networks. \[y=\text{F}(\text{X},\{W_{i}\})+\text{X} \tag{6}\]  
\[\text{F}(\text{X},\{W_{i}\})=y-\text{X} \tag{7}\]  
Figure 5: Faster R-CNN and MRS Faster R-CNN structure diagram. Figure 6: Residual module.