## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Segmentation Techniques
#### 3.2.1 Semantic Segmentation Techniques Based on Deep Learning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Research Object** & \multicolumn{2}{c}{**Technical Characteristics and Performance Indicators**} & **References** \\ \hline \multirow{4}{*}{Oil-seed camellia fruit} & This study used the convolutional nucleus of different scales to & \multirow{4}{*}{[56]} \\  & optimize YOLOv4-Tiny. It showed good stability under lighting & & \\  & conditions, and the recognition performance decreased under & & \\  & severe occlusion conditions. & & \\ \hline \multirow{4}{*}{Strawberry} & This study used CSPNet to optimize YOLOv4-Tiny. Its detection & \multirow{4}{*}{[57]} \\  & speed was increased by 25.93\%, the accuracy was reduced by only & & \\  & 0.62\%, and it was deployed in embedded systems with good & & \\  & performance. & & \\ \hline \multirow{2}{*}{Litchi} & The detection of lithi was achieved by using YOLOv5, with an & \multirow{2}{*}{[58]} \\  & mAP of 79.6\% and a recall rate of 75.25\% & & \\ \hline \multirow{4}{*}{Citrus} & This study identified and judged the maturity of citrus by & \multirow{4}{*}{[59]} \\  & combining ResNet34 and YOLOv5, with an accuracy rate & & \\  & of 95.07\% & & \\ \hline \multirow{4}{*}{Apple calyx} & This study optimized YOLOv5s through channel pruning and & \multirow{4}{*}{[60]} \\  & other techniques. The optimized model physical examination had & & \\  & been reduced by 71\%, while mAP had only been reduced & & \\  & by 1.57\%. & & \\ \hline \multirow{4}{*}{Green citrus} & CBAM-optimized YOLOv5 was used in this study to realize the & \multirow{4}{*}{[61]} \\  & identification and detection of green citrus. Its mAP was 98.23\%, & & \\  & and the recall rate was 97.66\%. & & \\ \hline \multirow{4}{*}{Apple} & This study adopted SE optimization YOLOv5s to realize apple & \multirow{4}{*}{[62]} \\  & identification under different occlusion conditions. Its mAP was 86.75\%, and the recall rate was 91.48\%. & & \\ \hline \multirow{4}{*}{SSD} & Apple, lithi, navel orange, citrus & This study used ResNet-101, migratory learning and random & \multirow{4}{*}{[64]} \\  & gradient descent garlic vendor to optimize SSD. Its mAP for the & & \\  & four fruits was 89.53\% & & \\ \hline \multirow{4}{*}{SSD} & Lingwu long jujuibe & In this study, DenseNet was used to optimize SSD and realize the & \multirow{4}{*}{[65]} \\  & identification of fruits, with an mAP of 96.6\% and a detection & & \\  & speed of 28.05 fps/s & & \\ \hline \multirow{4}{*}{Litchi} & This study was based on FSSD, optimizes the feature extraction & \multirow{4}{*}{[67]} \\  & network and realized the detection of lithi. Its AP was 55.79\%, & & \\  & and there were emissions and missed detection. & & \\ \hline \multirow{4}{*}{Apple, camellia oleifera fruit} & They used the RetinaNet model for apple and camellia oleifera & \multirow{4}{*}{[69,73]} \\  & fruit recognition, and their APs were 83.1\% and & & \\  & 87.9\%, respectively. & & \\ \hline \multirow{4}{*}{RetinaNet} & \multirow{4}{*}{Apple} & This study was based on RetinaNet, using the Res2 Net module to & \multirow{4}{*}{[70]} \\  & Apple & optimize the backbone, BiFPN to optimize the neck, and EloU Loss & \multirow{4}{*}{[72]} \\  & as the loss function to achieve the recognition of apples, and its AP & & \\ \cline{1-1}  & was improved by 5.02\% compared with that before optimization. & \\ \hline \multirow{4}{*}{Rosa roxburgh traitt} & This study was based on RetinaNe, optimizing the loss function & \multirow{4}{*}{[72]} \\  & and using the K-means + + clustering algorithm to optimize & \\ \cline{1-1}  & Anchor. It has an AP of 94.86\%, improving the original model & \\ \cline{1-1}  & by 1.8\%. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._tree branches. However, the stems of the guava were poorly segmented, which meant it was difficult for the picking robot to achieve collision-free picking based on the segmentation results.