## 3 Deep Learning-Based Target Recognition Techniques
### Vision-Based 3D Reconstruction Technology of Fruit
#### 3.3.1 Three-Dimensional Reconstruction Technology Based on Stereo Vision  
Stereo vision-based three-dimensional reconstruction technology uses two or more cameras to capture images from different angles based on the principle of stereo matching to reproduce three-dimensional scenes. The principle of binocular stereo vision 3D matching is shown in Figure 10. The application of 3D reconstruction technology based on stereo vision can effectively enhance the environment perception ability of the fruit-picking robot and increase its working efficiency. [PERSON] et al. [88] used a binocular stereo-vision system to obtain the 3D position of apples. The approach is to first detect the apples using Faster R-CNN on the images captured by the vision system, then segment the apples from the images based on color features, and finally, perform stereo matching to identify the three-dimensional spatial information of the apples. However, this approach is less effective when the fruit \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Research Object** & **Technical Characteristics and Performance Indicators** & **References** \\ \hline FCN & Guava & In this study, FCN was used to achieve image segmentation of guava, and the average accuracy was 89.3\%. & [75] \\ \hline DeepLabV3 & Litchi & In this study, DeepLabV3 was used to segment images into backgrounds, fruits and branches, and the detection accuracy of lychense was 83.33\%. & [8] \\ \hline DeepLabV3+ & Litchi & This study used Xception-65 to optimize DeepLabV3+ and realized the segmentation of litchi growing branches, with an MIoU of 76.5\%. & [80] \\ \hline SegNet & Apple & This study used SegNet to segment the trunks and branches of apple trees in the image, with segmentation accuracy of 92\% and 93\% and MIoU of 59\% and 44\%, respectively. & [82] \\ \hline \hline \multirow{4}{*}{Mask R-CNN} & \multirow{4}{*}{Strawberry} & In this study, Mask R-CNN was used to achieve the segmentation of \\  & & strawberries in the natural environment, with an AP of 95.78\% and a recall rate & [84] \\ \cline{2-4}  & & & of 95.41\% \\ \cline{2-4}  & \multirow{4}{*}{Apple} & This study used ResNet and DenseNet to optimize Mask R-CNN, which \\  & & realized apple segmentation. Its accuracy rate was 97.31\%, and the recall rate \\ \cline{2-4}  & & was 95.70\%. & \\ \cline{2-4}  & \multirow{4}{*}{Apple} & This study optimized Mask R-CNN using the ResNet of the fusion attention \\  & & mechanism and characteristic pyramid network. For apple segmentation under \\ \cline{1-1}  & & different lighting conditions and occlusion conditions, its mAP and recall rates \\ \cline{1-1}  & & were 91.7\% and 97.1\%, respectively. & \\ \hline \hline \multirow{2}{*}{Cherry tomato} & In this study, the segmentation of cherry tomatoes was realized by optimizing \\ \cline{1-1}  & & Mask R-CNN, with a segmentation accuracy of 93.76\% and 89.34\%, respectively. & [87] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of research results of deep-learning-based target segmentation methods. Figure 9: Flow chart of vision-based 3D reconstruction technology. is occluded. For the problem of occluded fruits, [PERSON] et al. [9] proposed a different solution by using two cameras to build a stereo vision system that identifies apples in orchards. The approach uses the RRM algorithm to identify the spatial 3D position of apples by taking images from different angles with different cameras in the stereo vision system. The system was tested in an actual orchard and showed good performance, but limited by lighting conditions, there were differences in the quality of images taken by the stereo vision system from different angles, which affected the recognition performance of the system. To address this problem, [PERSON] et al. [89] proposed a binocular-vision-based recognition system for lychee in unstructured environments.