## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.1 Classification-Based Two-Stage Target Detection Techniques  
[PERSON] et al. [21] introduced the R-CNN model inspired by Alexnet [22], which was a great breakthrough in target detection and laid the foundation for the later R-CNN series networks. The R-CNN is based on four steps: first, input an original image; second, generate about 2000 candidate regions based on the input image using the selective search [23] algorithm, and preprocess each candidate region into a uniformly fixed size; third, extract region features using the Alexnet network; and finally, classify the extracted region features using the SVM classification algorithm. [PERSON] et al. [21] showed through experiments that the mAP on the VOC 2012 dataset was 53.3%. The proposed R-CNN has greatly facilitated the development of target detection techniques, but it requires excessive time to generate candidate regions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traditional Digital Image Processing Techniques** & **Application to Crops** & \begin{tabular}{c} **Technical Characteristics** \\ **and Performance** \\ **Indicators** \\ \end{tabular} & **Limitations** & **References** \\ \hline Texture feature-based & Citrus, pineapple & \begin{tabular}{c} Bitter melon can \\ significantly segment the \\ fruit from the background, \\ with a combined \\ recognition success rate \\ of 85\% \\ \end{tabular} & \begin{tabular}{c} Vulnerable to the growth \\ environment of fruit trees, \\ fruit shading, etc. \\ \end{tabular} & [16; 17; 18] \\ Multi-feature fusion based & Oranges, apples, mangoes & \
\begin{tabular}{c} Makes up for the \\ shortcomings of a single \\ feature and can more \\ accurately segment the \\ fruit from the background, \\ with a combined \\ recognition success rate \\ of 90\% \\ \end{tabular} &
\begin{tabular}{c} Although the recognition \\ success is improved by \\ feature fusion, it cannot \\ offset the influence of \\ natural environmental \\ factors, such as lighting \\ conditions \\ \end{tabular} & [19; 20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: The evolution of deep learning-based target detection algorithms. To address the problem that R-CNN only has forward transmission and no shared computing, [PERSON] et al. [24] proposed a Spatial Pyramid Pooling Network (SPP-Net). SPP-Net realized the shared computation of convolutional features by introducing adaptive size pooling, which greatly reduced the amount of computation and improved the detection speed compared with R-CNN. In 2015, [PERSON] [25] proposed the Fast R-CNN target detection algorithm. This algorithm enhanced and optimized the detection speed and accuracy by combining R-CNN and SPP-Net. Fast R-CNN optimized SPP-Net in that it enabled adjustable parameters. The feature extraction network and classification algorithm in the R-CNN algorithm were optimized, and VGG16 was used in lieu of the AlexNet network, and the SoftMax classifier is used in lieu of the SVM classifier. Fusing R-CNN and SPP-Net, the Fast R-CNN excellent performance has been widely recognized in a large number of practices. [PERSON] et al. [26] improved Fast R-CNN for recognition detection of key organs in tomatoes by fusing automatically extracted RGB and grayscale image features. A double convolutional chain Fast R-CNN was proposed, and the highest average precision (AP) of this recognition algorithm was concluded to be 70.33%, 63.99%, and 44.95%, respectively, for the recognition of three key organs of tomatoes. The main reason why SPP-Net and Fast R-CNN take more time is that more specialized region proposals are generated by the selective search algorithm during operation. Therefore, a new algorithm for generating candidate regions, the Region Proposal Networks (RPN), is proposed. The sliding window adopted by the RPn algorithm effectively solves the problem of producing too many candidate regions and saves computing resources while each candidate region is given a score. To improve the performance of the target detection algorithm, [PERSON] et al.