ing at the problems of missed detection and low efficiency in manual classification and diagnosis of optical coherence tomography retina images, a deep learning-based convolutional network classification algorithm is proposed to construct joint multilayer features. First, retinal images are preprocessed using the mean shift and data normalization algorithm. The loss function weighting algorithm is combined to solve the data imbalance problem. Second, a lightweight deep separable convolution rather than an ordinary convolution layer is used to reduce the number of model parameters. Global average pooling replaces fully connected layers to increase spatial robustness, and different convolutional layers are used to build feature fusion layers to enhance feature circulation between layers. Finally, the SoftMax classifier is used for image classification. Experimental results show that the model can achieve 97%, 95%, and 97% in accuracy, precision, and recall, respectively, thereby reducing the recognition time. The proposed deep learning feature fusion-based method performs well in the classification and diagnosis of retinal images.

ed at the occlusion problem of target tracking in machine vision, an occlusion detection mechanism is introduced based on the original Distractor-Aware Tracking (DAT) algorithm framework, and a Detection-DAT (DDAT) algorithm is proposed. First, this mechanism extracts color characteristics of the target, calculates similarities between the target frames through color characteristics, and uses the similarity trends and the threshold values of the differences between the frames to determine whether the target has been occluded during tracking. Second, Naive Bayes and nearest neighbor classifiers are adopted to obtain the target frame in subsequent frames. Finally, similarity is applied to detect whether the target frame obtained by the two classifiers is the correct target frame. To verify the effectiveness of the algorithm, qualitative and quantitative comparisons with the DAT algorithm and other tracking algorithms were performed on the standard data set video sequence with occlusion properties.

ing to address the low identification accuracy of remote-sensing tree species of forests with a complex canopy and high density, a three-dimensional convolution neural network (3D-CNN) that can extract the stereoscopic features of hyper-dimensional data is introduced herein, and it can identify remote-sensing images. Furthermore, it is improved through residual network (ResNet) to build a 3D residual convolution neural network (3D-RCNN) to reduce the influence of degradation phenomenon and the inaccuracy caused by network depth. The sample set is constructed by combining GF-5 hyperspectral data (GF-5 AHIS)and GF-6 high spatial resolution data (GF-6 PMS), supplemented by forest resource data and field survey data. Then, a tree species recognition model is constructed based on the concept of 3D-RCNN. The experimental results show that compared with traditional 3D-CNN, the proposed 3D-RCNN increases the model network's density from 12 layers to 18 layers, which can deepen the network structure and alleviate network degradation. By combining GF-5 AHIS and GF-6 PMS, 3D-RCNN can effectively identify northern subtropical forest species, providing better recognition accuracy (91.72%) than traditional 3D-CNN (85.65%) and support vector machine algorithm (85.22%).

ing at the problem that increased difficulties in detection of ship detection in remote sensing images caused by the narrow and long shape, disorderly distribution and other characteristics, a ship target detection method based on faster region-convolution neural network (Faster R-CNN) is proposed in this paper. The method uses a two-way network to extract ship target features. In order to make the feature map fully integrate the low-level detail information and high-level semantic information, a multi-scale fusion feature pyramid network (MFPN) is used for feature fusion; in the candidate frame generation stage, an adaptive rotation region proposal network (AR-RPN) is proposed to generate a rotating anchor frame at the center of the target to efficiently obtain high-quality candidate frames. In order to improve the detection rate of the network to ship targets, the network is optimized with an improved loss function. The test results on the public ship data sets HRSC2016 and the DOTA show that the average accuracy of this method is 89.10% and 88.64%, respectively, which can well adapt to the shape and distribution characteristics of ships in remote sensing images.