## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.2 Classification-Based One-Stage Target Detection Techniques
The improved YOLOv3 model is used to replace the low-resolution layers in the original YOLOv3 model by using the DenseNet model, which effectively reduces the loss of feature information during the propagation of feature maps, mitigates gradient disappearance, enhances feature propagation, and promotes feature multiplexing and fusion. The tests showed that YOLOv3-DenseNet offered good detection performance for occluded and overlapped apples. Based on this, [PERSON] et al. [45] proposed a YOLO-Tomato model for detecting tomatoes in complex environments. The YOLO-Tomato model not only adopted the DenseNet model to promote feature reuse but also applied the circular bounding box (C-Bbox). The C-Bbox offers two advantages: first, the shape of tomatoes matches the C-Bbox more accurately. Secondly, the C-Bbox has fewer parameters, which reduces the complexity of the calculation. The experiments showed that YOLO-Tomato was able to reduce the effect of changing lighting conditions and offered better robustness. The YOLOv3-Tiny model is the lightweight version of the YOLOv3 model. Based on the YOLOv3 model, the YOLOv3-Tiny model streamlines the backbone network and other features for better real-time performance. To enable recognition systems of the picking robot to be mounted on small hardware devices, [PERSON] et al. [46] proposed a high-accuracy and high-speed deep YOLOv3-tiny network (DY3 TNet) target detection model that can be used for detecting kiwifruit in orchards under all-weather conditions. The DY3 TNet model is based on the YOLOv3-tiny model and improved by using multiple 1 \(\times\) 1 convolutional layers to reduce the operation load and increase the detection speed. The DY3 TNet target detection model was used for the detection experiments of kiwifruit images under different lighting conditions, and the AP was 90.05% under changing light conditions, offering good detection performance. In addition, [PERSON] et al. [47] proposed a Light-YOLOv3 model based on the improved YOLOv3 for green mango detection in complex environments. The model not only optimizes the residual network and NMS to different degrees but also incorporates the MSCA unit. These improvements make the computational speed of the Light-YOLOv3 model on the embedded platform five times faster than the original YOLOv3 model and also demonstrate good performance under different shaded lighting conditions, which provides technical support for improving the efficiency of mango-picking robots. YOLOv4 [48] offered four improvements over YOLOv3: first, it introduced Mosaic [49] data to enhance operations; second, it fused Darknet53 with CSPNet [50] and used it as a backbone network; third, it introduced the SPP network to expand the perceptual field; fourth, it used FPN+Path Aggregation Network (PAN) [51] to enhance feature fusion; and fifth, CIoU_loss was used as the loss function at the prediction end. These improvements significantly enhanced the detection performance compared to the YOLOv3 model. In reference [52], by using YOLOv3 and YOLOv4 to detect bananas under different lighting conditions, YOLOv4 was proven better than YOLOv3 in terms of detection accuracy and speed. Some researchers improved the YOLOv4 model by combining residual neural networks, densely connected networks, and attention mechanisms. [PERSON] et al. [53] proposed an RC-YOLOv4 model, which fused residual neural networks for tomato detection. The RC-YOLOv4 model fused ResNet networks in the backbone network of the original YOLOv4 model to construct a new backbone network, the R-CSPDarknet53. The C-SPP network replaced the original SPP network to reduce the loss of feature information. The experimental results showed that this improved approach greatly enhanced the success rate of the RC-YOLOv4 model for detecting tomatoes. [PERSON] et al. [54] proposed a YOLOV4-DenseNet model incorporating a densely connected network for cherry detection to address small target fruits.