Diabetic retinopathy (DR) is the prime cause of blindness in people who suffer from diabetes. Automation of DR diagnosis could help a lot of patients avoid the risk of blindness by identifying the disease and making judgments at an early stage. The main focus of the present work is to propose a feasible scheme of DR severity level detection under the MobileNetV3 backbone network based on a multi-scale feature of the retinal fundus image and improve the classification performance of the model. Firstly, a special residual attention module RCAM for multi-scale feature extraction from different convolution layers was designed. Then, the feature fusion by an innovative operation of adaptive weighting was carried out in each layer. The corresponding weight of the convolution block is updated in the model training automatically, with further global average pooling (GAP) and division process to avoid over-fitting of the model and removing non-critical features. In addition, Focal Loss is used as a loss function due to the data imbalance of DR images. The experimental results based on Kaggle APTOS 2019 contest dataset show that our proposed method for DR severity classification achieves an accuracy of 85.32%, a kappa statistic of 77.26%, and an AUC of 0.97. The comparison results also indicate that the model obtained is superior to the existing models and presents superior classification performance on the dataset.

Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.

This article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the keypoints precisely in complex weather scenes. In this article, we propose a novel approach, called Pose Estimation with Keypoints and Structures (PEKS), which leverages multiple intermediate representations to estimate the 6D pose. Unlike previous works, our approach simultaneously locates keypoints and structures to recover the pose parameter of aircraft through a Perspective-n-Point Structure (PnPS) algorithm. These representations integrate the local geometric information of the object and the topological relationship between components of the target, which effectively improve the accuracy and robustness of 6D pose estimation. In addition, we contribute a dataset for aircraft pose estimation which consists of 3681 real images and 216,000 rendered images. Extensive experiments on our own aircraft pose dataset and multiple open-access pose datasets (e.g., ObjectNet3D, LineMOD) demonstrate that our proposed method can accurately estimate 6D aircraft pose in various complex weather scenes while achieving the comparative performance with the state-of-the-art pose estimation methods.

No abstract

In computer vision, convolution and pooling operations tend to lose high-frequency information, and the contour details will also disappear with the deepening of the network, especially in image semantic segmentation. For RGB-D image semantic segmentation, all the effective information of RGB and depth image can not be used effectively, while the form of wavelet transform can retain the low and high frequency information of the original image perfectly. In order to solve the information losing problems, we proposed an RGB-D indoor semantic segmentation network based on multi-scale fusion: designed a wavelet transform fusion module to retain contour details, a nonsubsampled contourlet transform to replace the pooling operation, and a multiple pyramid module to aggregate multi-scale information and context global information. The proposed method can retain the characteristics of multi-scale information with the help of wavelet transform, and make full use of the complementarity of high and low frequency information. As the depth of the convolutional neural network increases without losing the multi-frequency characteristics, the segmentation accuracy of image edge contour details is also improved. We evaluated our proposed efficient method on commonly used indoor datasets NYUv2 and SUNRGB-D, and the results showed that we achieved state-of-the-art performance and real-time inference.