Recovering depth information of objects from two-dimensional images is one of the very important and basic problems in the field of computer vision. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object’s depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the most appropriate pair of straight line segments are chosen, and also reduce computation, convex hull theory and knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

Strip surface defects have large intraclass and small interclass differences, resulting in the available detection techniques having either a low accuracy or very poor real-time performance. In order to improve the ability for capturing steel surface defects, the context fusion structure introduces the local information of the shallow layer and the semantic information of the deep layer into multiscale feature maps. In addition, for filtering the semantic conflicts and redundancies arising from context fusion, a feature refinement module is introduced in our method, which further improves the detection accuracy. Our experimental results show that this significantly improved the performance. In particular, our method achieved 79.5% mAP and 71 FPS on the public NEU-DET dataset. This means that our method had a higher detection accuracy compared to other techniques.

It is one of very important and basic problem in compute vision field that recovering depth information of objects from two-dimensional images. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the best appropriate a pair of straight line segments are chose and reduce the computation, the theory of convex hull and the knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

In this work, we propose a new crack image detection and segmentation method for addressing the issues regarding the poor detection of crack structures in certain complex background conditions, such as the light and shadow, and the easy-to-lose details in segmentation. This method can be categorized into two phases, where the first one is the coding phase. In this phase, the channel attention mechanism and crack characteristics, using the correlation channel with different scales increasing the network robustness and ability of feature extraction, have been introduced to decouple the channel dimension and space dimension. It also avoids underfitting caused by information redundancy during the jumping connection. In the second stage, i.e., the decoding stage, the spatial attention mechanism has been introduced to capture the crack edge information through the global maximum pooling and global average pooling of the high-dimensional features. Then, the correlation between the space and channel has been recovered through multiscale image information fusion to achieve accurate crack positioning. Furthermore, the Dice loss function has been employed to solve the problem of pixel imbalance between the categories. Finally, the proposed method has been tested and compared with existing methods. The experimental results illustrate that our method has a higher crack segmentation accuracy than existing methods. Furthermore, the mean intersection over the union ratio reaches 87.2% on the public dataset and 83.9% on the self-built dataset, and it has a better segmentation effect and richer details. It can solve the problem of crack image detection and segmentation under a complex background.

DEtection TRansformer (DETR) and its variant models degrade the object detection performance due to the inability to provide the object position a priori and the lack of shape deviation supervision between prediction boxes and ground truth boxes, which makes some of the shape differences between prediction boxes and ground truth boxes too large. To address this problem, we propose an end-to-end object detection method (SF-DETR) based on shape correction and feature selection cross-attention. First, a feature selection layer is introduced to provide the model with a priori object position by combining classification confidence and intersection over union score to filter high-quality prediction boxes and constrain the computational range of decoder cross-attention. Second, the shape bias is numericalized and a shape correction loss function is proposed to supervise the shape bias and ensure that the shape bias is effectively corrected. Finally, we conducted experiments on three public datasets, MS COCO, Caltech Pedestrians, and BIT-Vehicle, and the experimental results show that the method in this paper not only provides the object position a priori effectively but also reduces the shape bias significantly, which improves the prediction box quality and object detection accuracy.

Recovering the depth information from a single image is a fundamental problem in computer vision field and has broad application prospects. To solve the problem that the accuracy of the depth recovered from a single image is not high enough, especially when there are several edges very close or intersecting, or when the edge is weak, a novel method to depth measurement is proposed in this article. Four steps are included in our method. Firstly, we obtain depth value of object edge point indirectly by measuring the defocusing degree of the object edge point. The gradient information of the 8 directions of edge point are employed during the process of measuring. Secondly, wavelet analysis is used to judge whether the measured depth value needs to be corrected or not. If necessary, it is corrected according to our formula. A sparse depth map is got when the depth values of all of edge points are measured and are corrected if necessary. Thirdly, the joint bilateral filter is employed to refine the sparse depth map. Lastly, the sparse depth map is extended to a dense depth map by the method of Matting Laplacian. The results of experiments show our method is effective.