## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.2 Classification-Based One-Stage Target Detection Techniques
How to further improve the YOLOv5 model to adapt it to the actual working environment of picking robots is an important research direction. [PERSON] et al. [61] proposed a YOLOv5-CS model for the detection of green citrus in natural environments. The YOLOv5-CS model is improved in that it optimizes data augmentation and loss function while incorporating the cbam attention mechanism. [PERSON] et al. [62] proposed an improved YOLOv5s-based lightweight detection model for apple detection to address the problem that occluded fruits might prevent the picking robot from performing its task. The improved YOLOv5s model improves the detection accuracy of the model by enhancing the CSP module and the initial anchor frame size of the backbone network and proposes to incorporate the SE module into the backbone network. It is shown experimentally that the improved YOLOv5s model compresses the volume by 9.29%, while the mAP value improves it by 5.05%. SSD model [PERSON] et al. [63] proposed a Single Shot Multi-box Detector (SSD) target detection model, whose core idea is to implement a feature mapping by designing a small convolutional filter and generating and predicting the category and offset of the bounding box. The SSD model uses multi-scale prediction, using a large-scale feature map to detect small objects and a small-scale feature map. The SSD model increases the detection speed without compromising the detection accuracy compared to the classification-based two-stage target detection model, which basically achieves a balance between the two, and many researchers have applied it to fruit target detection. [PERSON] et al. [64] used the ResNet-101 model to optimize the backbone network, increased the depth of the feature extraction network, and optimized the SSD model by migration learning and stochastic gradient descent techniques. The system also achieves target detection of litchi, apple, navel orange, and emperor citrus by yielding an mAP of 89.53%, which is higher than that of the original SSD network. The detection effect of different kinds of fruits in the experiment is not substantially different, which reflects that the system has high robustness. [PERSON] et al. [65] proposed a more lightweight and improved SSD model for the detection of Lingwu jujube in the complex operating environment of the Lingwu jujube picking robot. Through experiments, it was shown that the mAP of the improved SSD model was 96.6% for detecting the target of Lingwu jujube, which further promoted the development of intelligent picking technology for Lingwu jujube. The SSD detection model uses feature pyramids to detect targets at different scales, but the method makes it difficult to fuse features at different scales, and the feature map characterization ability of small targets overlaps, so it offers poor results in detecting small targets. The Feature Fusion Single Shot Multibox Detector (FSSD) [66] is a feature fusion approach used to solve the problem of difficult detection of small targets. On this basis, in reference [67], multi-output optimization was used for the convolutional layer to obtain a recognition model for a small target, and the model was applied to the detection of small litchi in images taken by UAVs, proving its effectiveness. RetinaNet  
In 2007, [PERSON] et al. [68] proposed the RetinaNet model, which was an effective one-stage detection algorithm. The RetinaNet model utilized the FPN neck network for feature fusion, the RestNet network as the backbone network for feature extraction, and the FCN for the completion of regression and prediction. The initial stage of the regression classification procedure had a sample imbalance issue since the model did not create candidate regions. The RetinaNet model's prediction process used the Focal Loss Loss Function, which, by adjusting the weights of the classification of various samples, successfully worked out the issue of an imbalance in the amount of positive and negative samples during the model's training process.