## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.2 Classification-Based One-Stage Target Detection Techniques
Facing the complex environment in the actual orchard, it identified apples [69; 70; 71], Rosa rokburghii fruit [72], and camellia oleifera fruit [73] in a real orchard, RestNet performed better. To improve RetinaNet's ability to detect apples in complex environments, [PERSON] et al. [70] enhanced its backbone network, neck network, and loss function by optimizing each of them. These optimizations increased the feature extraction ability and rate of convergence, and the test indicated a 5.02% improvement in mAP. In addition, [PERSON] et al. [72] suggested various improvement plans for the RetinaNet model and used the enhanced model to recognize Rosa roxburghii tratt. By adopting the K-means ++ clustering algorithm, the technique was provided to optimize the Anchor scale as well as the bias in the focal loss function. The optimized model recognized six types of prickly pear images, yielding an average recognition rate of 94.86%, which was 1.8% higher than the original model. A comparison of the research results of deep learning-based target detection methods is shown in Table 2. Through comparative analysis, two conclusions can be drawn: (a) deep-learning-based target detection methods improve the detection performance in varying degrees compared with traditional digital image processing techniques and are more suitable for complex natural environments; (b) although deep learning-based target detection methods offer strong feature extraction capability, they are subject to environmental interference and thus require a large number of data sets for training, and the training time is long. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Research Object** & **Technical Characteristics and Performance Indicators** & **References** \\ \hline \multirow{8}{*}{\ \begin{tabular}{c} Faster R-CNN \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Mango flower spikes \\ \end{tabular}  } & This study used Faster R-CNN to identify mango spikes but with a low & [28] \\  & & accuracy of 66\% and a recall rate of 48\%. & [28] \\ \cline{2-4}  & \multirow{2}{*}{Apple} & This study used VGG16 to optimize Faster R-CNN, which had good & [29] \\  & & robustness to blocked apples. Its mAP was 87.9\%. & [30] \\ \cline{2-4}  & \multirow{2}{*}{Kiwifruit} & This study used VGG16 to optimize Faster R-CNN, which showed good & [30] \\  & & robustness for fruits under different lighting conditions, and its mAP was 87.61\%. & [31] \\ \cline{2-4}  & \multirow{2}{*}{Passion fruit} & This study enhanced Faster R-CNN’s ability to identify small targets by integrating global and local features, with an accuracy of 93.1\% and a recall rate of 96.2\%. & [31] \\ \cline{2-4}  & \multirow{2}{*}{Kiwifruit} & This study used ZFNet, backpropagation and stochastic gradient descent & [32] \\  & & techniques to improve Faster R-CNN to realize kiwifruit image detection, with an AP of 89.3\%. & [33] \\ \cline{2-4}  & \multirow{2}{*}{Coconut} & This study utilized ResNet-50 to optimize Faster R-CNN, which showed & [33] \\  & & good robustness for complex environments. Its mAP was 89.4\%. & [33] \\ \cline{2-4}  & \multirow{2}{*}{Tomato} & This study used ResNet-50 and K-means clustering algorithms to optimize & \\  & & Faster R-CNN and achieved the detection of tomato organs. Its mAP was 90.7\%. & [34] \\ \hline \multirow{4}{*}{Green mango} & This study used YOLOv2 to identify green mangoes, and its AP was 86.43\%. However, the recognition effect would decrease in complex environments if the fruit was blocked. & [39] \\ \cline{2-4}  & \multirow{2}{*}{Green mango} & This study used Tiny-YOLO to optimize YOLOv2, which improved the & [40] \\  & & recognition performance in fruit overlapping scenarios, and the accuracy & [40] \\ \cline{2-4}  & \multirow{2}{*}{Litchi} & This study adopted lithi identification at different brightness of YOLOv3, & [10] \\  & & and its mAP was 96.43\%. & [10] \\ \cline{2-4}  & \multirow{2}{*}{Apple} & This study used DensNet to optimize YOLOv3 to improve the recognition & [44] \\  & & accuracy of fruit occlusion scenarios.