A multi-scale 2D-Adaboost algorithm was proposed to solve the problem that the microscopic images of Chinese medicinal materials powder contain a large number of fine features and background interference factors， which leads to excessive changes in the same medicinal materials （large differences within the class） and too similar features among various medicinal materials （small differences between the classes）. Firstly， a global-local feature fusion backbone network architecture was constructed to extract multi-scale features better. By combining the advantages of Transformer and Convolutional Neural Network （CNN）， this architecture was able to extract and fuse global and local features at various scales effectively， thereby improving the feature capture capability of the backbone network significantly. Secondly， the single-scale output of Adaboost was extended to multi-scale output， and a 2D-Adaboost structure-based background suppression module was constructed. With this module， the output feature maps of each scale of the backbone network were divided into foreground and background， thereby suppressing feature values of the background region effectively and enhancing the strength of discriminative features. Finally， an extra classifier was added to each scale of the 2D-Adaboost structure to build a feature refinement module， which coordinated the collaborative learning among the classifiers by controlling temperature parameters， thereby refining the feature maps of different scales gradually， helping the network to learn more appropriate feature scales， and enriching the detailed feature representation. Experimental results show that the recognition accuracy of the proposed algorithm reaches 96.85%， which is increased by 7.56， 5.26， 3.79 and 2.60 percentage points， respectively， compared with those of ConvNeXt-L， ViT-L， Swin-L， and Conformer-L models. The high accuracy and stability of the classification validate the effectiveness of the proposed algorithm in classification tasks of Chinese medicinal materials powder microscopic images.

Cross-domain discrepancy frequently hinders deep neural networks to generalize to different datasets. In order to improve the robustness of shadow detection, a novel unsupervised domain adaptive shadow detection framework was proposed. Firstly, in order to reduce the data bias between different domains, a multi-level domain adaptive model was introduced to align the feature distributions of source domain and target domain from low level to high level. Secondly, to improve the model ability of soft shadow detection, a boundary-driven adversarial branch was proposed to guarantee the structured shadow boundary was also able to be obtained by the model on the target dataset. Thirdly, the entropy adversarial branch was combined to further suppress the high uncertainty at shadow boundary of the prediction result, so as to obtain an accurate and smooth shadow mask. Compared with the existing deep learning-based shadow detection methods, the proposed method has the Balance Error Rate (BER) averagely reduced by 10.5% and 18.75% on ISTD dataset and SBU dataset respectively. The experimental results demonstrate that the shadow detection results of the proposed algorithm have better boundary structure.

Aiming at the problems that a wide variety of Chinese medicinal materials have small samples, and it is difficult to classify the vessels of them, an improved convolutional neural network method was proposed based on multi-channel color space and attention mechanism model. Firstly, the multi-channel color space was used to merge the RGB color space with other color spaces into 6 channels as the network input, so that the network was able to learn the characteristic information such as brightness, hue and saturation to make up for the insufficient samples. Secondly, the attention mechanism model was added to the network, in which the two pooling layers were connected tightly by the channel attention model, and the multi-scale cavity convolutions were combined by the spatial attention model, so that the network focused on the key feature information in the small samples. Aiming at 8 774 vessel images of 34 samples collected from Chinese medicinal materials, the experimental results show that by using the multi-channel color space and attention mechanism model method, compared with the original ResNet network, the accuracy is increased by 1.8 percentage points and 3.1 percentage points respectively, and the combination of the two methods increases accuracy by 4.1 percentage points. It can be seen that the proposed method greatly improves the accuracy of small-sample classification.

Aiming at the problem that the security surveillance cameras have been hidden by leaves, a leaf occlusion detection algorithm based on Support Vector Machine (SVM) was proposed. The algorithm contains three steps. First, the regions of the leaf existing in the video were segmented. The accumulated frame subtraction method was applied to achieve this purpose. Second, the color and area information of the whole video image and the segmented regions were extracted as the key features. Third, these features were used for modeling and detecting obstacle occlusion by SVM. For all the collected samples, the detection accuracy of this method can reach up to 84%. The experimental results show that the proposed algorithm can detect the leaf occlusion in security surveillance video effectively.

A new method based on watershed algorithm was proposed to raise the segmentation accuracy of the crop disease leaf images. At first, distance transformation and watershed segmentation were conducted on the binary crop disease leaf images to get the background marker, and the preliminary foreground markers were generated by extracting the regional minimum from the reconstructed gradient images, and then some fake foreground markers were eliminated by the further filter. In the next step, both background markers and foreground markers were imposed on the gradient image by the compulsive minimum algorithm. At last, the watershed transformation was carried out on the modified gradient image. Lots of cucumber disease leaf images were segmented effectively using the method. The results of experiment indicate that disease spots can be separated precisely from the crop leaf images. Additionally, the segmentation results are not influenced by leaf texture and its accuracy is up to more than 90 percent, so the method has certain validity and practical value.