Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead to a large amount of information lost. The loss of object information is particularly serious in the encoding and decoding process. This information loss leads to the estimated depth maps lacking object structure detail and have non-clear edges. Especially in a complex indoor environment, which is our research focus in this paper, the consequences of this loss of information are particularly serious. To solve this problem, we propose a Dense feature fusion network that uses a feature pyramid to aggregate various scale features. Furthermore, to improve the fusion effectiveness of decoded object contour information and depth information, we propose an adaptive depth fusion module, which allows the fusion network to fuse various scale depth maps adaptively to increase object information in the predicted depth map. Unlike other work predicting depth maps relying on U-NET architecture, our depth map predicted by fusing multi-scale depth maps. These depth maps have their own characteristics. By fusing them, we can estimate depth maps that not only include accurate depth information but also have rich object contour and structure detail. Experiments indicate that the proposed model can predict depth maps with more object information than other prework, and our model also shows competitive accuracy. Furthermore, compared with other contemporary techniques, our method gets state-of-the-art in edge accuracy on the NYU Depth V2 dataset.

Identification of a consumer’s intent has a vital impact on commodity recommendation, selection of hot drainage commodity, website layout, and link settings. Most of the present studies on user intent are considered static. Specific intent is accompa

Binocular stereo matching, a computer vision task typically using cost volume constructed from the left and right feature maps to estimate disparity and depth, is widely applied in 3D reconstruction, autonomous driving and robotics navigation. Though recent study brings an awareness of the convolution neural networks and the attention algorithms used in this field can make great progress, it is still difficult to satisfy the demand of high-precision applications due to many reasons. Study finds that the exist methods usually incline to ignore the intermediate feature map of other scales, pay less attention to the relationship between left and right feature maps and even just tend to use one type of cost volume to train the model. In this article, we mainly focus on solving the three rproblems mentioned above. Firstly, we present the Multi-scale Feature Extraction and Fusion Module (MFEFM) to get the informational feature maps via fusing all scale feature maps. And then we design the Effective Channel Attention Module (ECAM) applied to better capture and utilize the channel-wise independencies. Finally, we adopt the Hybrid Cost Volume Computation Module (HCVCM) to construct and aggregate cost volume. With these solutions, we build an end-to-end stereo matching network named HCVNet. Comparison with other state-of-the-art models, it can achieve 0.714 EPE on SceneFlow dataset, descending PSMNet (1.09 EPE) by 37.6%.

In the field of measuring parameters of mobile communication base station antenna, most of its methods share some deficiencies to a different extent. The traditional ones suffer from inferior efficiency and low safety coefficient while the state-of-the-art ones reveal problems of unsatisfying operational speed and accuracy. To address these problems, a high-speed and high-precision measuring system has been proposed with AntennaNet to speed up instance segmentation. First, the dual attention mechanism module is designed and adopted in AntennaNet, which can promote the accuracy and efficiency of the detection and segmentation of mobile base station antenna, while the PointRend module with down-tilt fitting is also employed to smooth the edges of masks segmented from antenna images and measure the parameters data. Second, transfer learning is introduced to ameliorate overfitting and training difficulty caused by small samples. Third, threshold, pixel coordinates, and least square are applied to ascertain the number of antennas and measure the relevant parameters. Finally, this high speed and high precision measuring system is complete with the combination of android App and UAV. Experimental results fully attest to the preponderance of the proposed method in its significant improvement of segmentation speed, fitting accuracy speed, and fitting accuracy of the antenna, whose relevant parameters of the fitting also conform to industry standards. Our method not only ensures staff safety but also improves social efficiency, which revolutionized the way of measuring antenna parameters of mobile communication base stations.

Stereo matching estimates the disparity between a pair of rectified left and right images. It plays an important role in robot navigation, autonomous driving, and other related tasks. Nowadays, convolutional neural networks based on deep learning have greatly improved the performance of stereo matching. However, there are still many challenges. One of them is that current stereo models mostly generate a single scale cost volume based on costly 3D convolutions method or quick 2D convolutions method, but neither of these two methods can achieve a fair trade-off between quality and time. In this paper, we propose to construct the multi-scale hybrid cost volume which aims at achieving fast speed while maintaining comparable accuracy. Concretely, we generate a correlation cost volume and a concatenation cost volume respectively, and then integrate together to form a hybrid cost volume which can significantly improve the accuracy and reduce the computational complexity. At the multi-scale level, we generate three hybrid cost volumes at different scales and then aggregate them by 2D convolutions which are faster than 3D convolutions. In addition, we adopt 2D CNN stacked hourglass with fused cost volume for cost aggregation. Specifically, the proposed method provides competitive performance with state-of-the-art methods, while being faster than most top-performing methods (e.g., $3.7\times $ than PSMNet, $8.1\times $ than GCNet, $16.3\times $ than GANet). Extensive experiments of our model on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the competitiveness of our results.