## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.2 Classification-Based One-Stage Target Detection Techniques  
(1) YOLO series  
In 2015, [PERSON] et al. [35] proposed the first regression-based one-stage target detection algorithm, You Only Look Once (YOLO), based on the GoogLeNet [36] model. Although YOLO has the biggest advantage over the classification-based two-stage target detection algorithm in terms of fast detection speed, it still has shortcomings in other aspects. First, because YOLO generates a limited number of grid cells, and each grid cell can only correspond to one category, YOLO is less effective in detecting dense and small objects. Second, YOLO is less effective in detecting objects when the size of the predicted objects differs greatly from the size of the training set. Subsequently, many scholars have improved the YOLO algorithm and further proposed YOLOv2, YOLOv3, YOLOv4, YOLOv5, and other algorithms. YOLOv2 [37] addresses three aspects of YOLO: detection accuracy, detection speed, and the number of detected objects, and improves the detection performance of the model by introducing structures such as Batch Normalization (BN) layers [38], anchor boxes, and improving the backbone network. [PERSON] et al. [39] improved the detection performance of the model by using the YOLOv2 target detection model for target detection of green mangoes by firstly taking images of a single mango species by UAV and, secondly, feeding the images into the YOLOv2 model for training, eventually identifying that the mAP of the training model was 86.4%. However, several experiments confirmed that its detection effect was reduced when there were too many mangoes causing fruit shading and poor lighting conditions. To address this problem, reference [40] proposed an improved YOLOv2-based detection approach for green mangoes by using a densely connected Tiny-YOLO-dense network structure to fuse features. By training this model, the accuracy for green mango detection exceeded 90%, and the improved target detection model offered better detection performance than Faster RCNN, especially in the case of background occlusion or fruit overlapping. However, this approach requires manual annotation of the foreground regions of the samples that are obscured or overlapped. Although YOLOv2 offers various improvements over YOLO, the model cannot perform multi-scale prediction. YOLOv3 [41] achieved multi-scale prediction by introducing Feature Pyramid Networks (FPN) [42]. Inspired by ResNet [43], the DarkNet53 [41] network is designed as the backbone network based on DarkNet19. Because of the excellent target detection performance of YOLOv3, many researchers have applied it to picking robots and employed it for fruit detection. To improve the efficiency of picking robots, [PERSON] et al. [10] used the YOLOv3 model to detect lithchi fruit during the night and tested the YOLOv3 model by using YOLOv3, SSD, and Faster R-CNN for images with high luminance, normal luminance, and low luminance, respectively. The YOLOv3 model yielded the best detection performance. Its lowest AP value was 89.3% in the low luminance with an average detection time of 0.026 s. Due to the complex growth environment of fruits, there are often cases where the background objects occluded fruits, or fruits overlap, or the fruits and background objects are highly similar. This further complicates the task of picking robots. Therefore, it is vital to improve the detection of target fruits by picking robots in complex environ ments. To address the problems of fruit occlusion by branches and leaves, fruit overlapping, and features that change with the growth cycle and thus affect the detection performance of the recognition system, [PERSON] et al. [44] proposed an improved YOLOv3 model for apple detection.