## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Segmentation Techniques
#### 3.2.2 Deep Learning-Based Instance Segmentation Techniques  
Instance segmentation provides different labels for separate instances of the same class of objects, so the image information provided by instance segmentation is more detailed than that offered by semantic segmentation. The most representative model for segmentation, for instance, is the Mask R-CNN [83] instance segmentation model, which consists of a backbone network ResNet-FPN [42; 43], a region proposal network RPN and three branches. The operation process is depicted in Figure 8. The ResNet-FPN extracts the features of the input image. The RPN generates the RoI (Region of Interest), which may contain the detected target based on the features of the image. Finally, the FC and FCN in the three branches perform the target classification and instance segmentation.  
Figure 7: Diagram of SegNet architecture.  
By comparing the detection accuracy of different residual networks as backbone networks, [PERSON] et al. [84] selected ResNet-50 to replace the ResNet-101 network of the original Mask R-CNN instance segmentation model to obtain more features of the image and improve the performance of the visual recognition system of the picking robot. The experiments showed that the improved Mask R-CNN model had a large improvement in detection speed compared with the previous one, and the MIoU of instance segmentation was 89.85%, which offered better generality and robustness in complex environments such as different illumination conditions, occlusion, and fruit overlapping. To further improve the target fruit segmentation accuracy and recognition speed, [PERSON] et al. [85] proposed an improved Mask R-CNN instance segmentation model by combining ResNet and DenseNet and used it for image instance segmentation of apples. The improved Mask R-CNN model not only deepens the depth of the model but also preserves more features of the target fruit in the image and reduces the number of parameters, and the recognition accuracy and recall of the model are 97.31% and 95.70%, respectively, as shown by experiments. However, [PERSON] et al. [86] took a different improvement approach to recognize apples in complex environments by using the ResNet-50 network and FPN instead of the backbone network of the original Mask R-CNN model and also introduced an attention mechanism by using transformer attention. The improved Mask R-CNN model is more accurate. The mAP and recall rates of instance segmentation were 91.7% and 97.1% under different lighting conditions, occlusion, and fruit overlapping. In addition, in the process of recognition, the spatial constraint relationship between the target fruit and the picked stem is fully utilized to obtain more accurate information about the fruit location. To this end, [PERSON] et al. [87] used RGB image and depth image data fusion at the input side while optimizing the RPN of the Mask R-CNN model. The experiments showed that the optimized model had an accuracy of 93.76% and 89.34% for the segmentation of fruits and stems, respectively, and the recognition time of a single image was 0.04 s.