## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.2 Classification-Based One-Stage Target Detection Techniques
The YOLOV4-DenseNet model uses DenseNet in lieu of the CSPDarknet53 utilized in the original YOLOV4 and adopts Leaky ReLU as the loss function to promote feature reuse and fusion to improve the network's detection performance for cherries. Through experiments using images of cherries, the detection performance of the YOLOV4-DenseNet model is proven to be better, which can realize intelligent picking and improve the efficiency of picking robots. To make the model easier to deploy in small embedded devices, the YOLOv4 model can be simplified using the channel pruning technology. This approach is to simplify the model by identifying and distinguishing the channels of the network, removing less important channels, and retaining important channels, thereby reducing the parameters to be stored by the model. [PERSON] et al. [55] proposed an approach to improve the YOLOv4 model in combination with the channel pruning algorithm for the detection of apple blossoms. The improved model achieved excellent results in terms of being lightweight, especially in that the number of parameters was reduced by 96.74% while the accuracy was maintained. In addition, as a lightweight version of the YOLOv4 model, the YOLOv4-tiny model has a simpler structure and fewer parameters, making it more suitable for deployment in small mobile terminals. [PERSON] et al. [56] proposed a YOLO-Oleifera model based on the improved YOLOv4-tin model for the detection of oil-seed camellia fruit. The YOLO-Oleifera model adopts two measures to optimize the clustering algorithm and adds a small-scale convolution kernel to optimize the YOLOv4-tin model, and the optimized model eliminates the shortcomings that fall into local optimization and enhances the operation speed. In order to meet the real-time requirements of the picking robot, the detection speed of the model is further improved. [PERSON] et al. [57] proposed an RTSD-Net model for the detection of strawberries by reducing the convolutional layer of the YOLOv4-tin model backbone network. Experiments showed that the RTSD-Net model has the advantage of lightweight technical specifications without significantly compromising accuracy. YOLOv5 implements the same Mosaic data enhancement on the input side and introduces adaptive anchor frame calculation to facilitate the identification of targets in different sizes. In addition, YOLOv5 introduces the Focus module for image slicing operation and designs two sets of CSPNet, which are applied in Backbone and Neck, respectively. On the output side, YOLOv5 adopts GIoU_loss as the loss function of the Bounding box. Comparing the detection performance of YOLOv5 and YOLOv4, there is not much difference between them in terms of accuracy, except that YOLOv5 performs better in terms of detection speed and is preferred by many researchers and also widely used in fruit recognition detection. In the literature [58], the YOLOv5 model can be used to detect lithchi fruits and detect the main stem of picking, effectively improving the picking efficiency of lithchi-picking robots. To improve the intelligence of picking robots, [PERSON] et al. [59] judged whether the fruit was pickable by ripeness, achieved the recognition detection of citrus by using the YOLOv5 model and then combined the RGB image information and the 4-pass ResNet34 network to distinguish the ripeness of the target fruit, and discovered through experiments that the accuracy of the modified approach was 95.07%. In addition, migration learning is an efficient way to improve detection speed. [PERSON] et al. [60] proposed a YOLOv5s-based detection model for apple calyx by using migration learning and channel pruning. The improved YOLOv5s model reduced the model parameters and weight volume by about 71% and substantially improved the computational speed to obtain a detection accuracy of 93.89%. In addition, target fruit detection in complex environments is a significant challenge for picking robots.