## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Detection Techniques
#### 3.1.1 Classification-Based Two-Stage Target Detection Techniques
[27] proposed the Faster R-CNN target detection model, which included the feature extraction network, RPN, and Fast R-CNN. The Faster R-CNN consists of multiple steps of candidate region generation, feature extraction, and classification through RPN to achieve end-to-end training, and its detection performance is substantially improved. Many researchers have already applied it to picking robots. For the problem of different ripening times of mangoes, [PERSON] et al. [28] detected mango flower spikes by using the Faster R-CNN model to facilitate the picking robot to select targets for early harvest. However, the experiments showed that the algorithm offered a low mAP value and unsatisfactory detection accuracy. For circumstances where the target fruit is occluded, [PERSON] et al. [29] classified the detection of the fruit on the tree by using the Faster R-CNN model, which helped the picking robot plan the picking path plan as well as avoid damage to its end-effector from the obstacles during the picking process. Taking apples as the research object, the system classified them into four categories and proved the average classification accuracy of 87.9% for the four categories through experiments. To address the problem of changing lighting conditions caused by the changing of time, [PERSON] et al. [30] developed a vision recognition system for picking robots that work all day long. By constructing a Faster R-CNN model and using images of kiwifruit collected under different lighting conditions for training, the accuracy of the test was 87.16%, and the model was able to recognize different kinds of fruits with good robustness. Since the PRN generation candidate region becomes a larger region after mapping from the anchor of the feature map against the original image, the Faster R-CNN algorithm does not work well for small targets. Many researchers have improved and optimized the Faster R-CNN algorithm by addressing this problem. For example, [PERSON] et al. [31] proposed a Multi-scale Faster R-CNN (MS-FRCNN) based on fusing local and global information. By using color feature information and depth information in images captured by RGB-D cameras, it improves the Faster R-CNN model for small target passion fruit. This approach proposes a new measure for the Faster R-CNN model for small target detection difficulty. In addition, it is challenging for Faster R-CNN to successfully detect fruits in the presence of target occlusion and complex backgrounds with different target morphology and sizes. Therefore, some researchers have proposed different solutions to this problem. To address the problems of slow recognition and poor robustness of the recognition system, [PERSON] et al. [32], based on the Faster R-CNN, combined ZFNet and backpropagation to extract features from the model, together with the gradient random descent technique to speed up the convergence of the model. It was demonstrated through experiments that these improvements adapt better to the natural environment with changing lighting conditions and have better robustness against the subjectivity and limitations of artificial feature selection. To improve the recognition accuracy and reduce the leakage rate in the case of high similarity between target fruit and background, [PERSON] et al. [33] proposed a Faster R-CNN improvement method for detecting coconuts. The method first used image enhancement technology to enhance the collected images and then used ResNet-50-optimized Faster R-CNN model for training and finally achieved better detection results. On this basis, [PERSON] et al. [34] employed the K-means clustering algorithm for optimization and used it to detect tomato organs, and the mAP of experimental results was significantly improved.