Depth or disparity estimation plays an important part in computer graphics and computer vision in recent years. Light field imaging has been widely used in the field of depth or disparity estimation because it contains information on light direction and intensity which can provide dense depth estimation. This paper proposes the SROACC-Net for light field structured light disparity estimation based on the OACC-Net with occlusion-aware cost constructor, where squeeze-andexcitation residual net (SE-ResNet) module is added to improve the accuracy. Moreover, Huber-SSIM loss function is designed to boost the performance of the model. The experimental results demonstrate that the SROACC-Net outperforms the OACC-Net in light field structured light depth prediction. The SROACC-Net under light field structured light provides a promising way for depth estimation in computer graphics and computer vision.
Garbage pollution is a very difficult problem in environmental governance. Due to the many sources of garbage pollution and a wide range of impacts, this problem is only slow to solve by human means. In order to improve the automation of garbage disposal, on the one hand, this paper proposes a garbage detection method based on CNN (convolutional neural network) using multi-layer feature processing. On the other hand, the detection algorithm is combined with an industrial robot to form a complete garbage sorting system. This paper uses the one-stage idea to first optimize the backbone structure to improve the extraction effect of shallow features. Then the attention module is introduced to make the network pay more attention to information that plays a key role in garbage detection. Finally, a multi-layer feature fusion method is used to combine the features of the shallow network with the features of the deep network to generate a fused feature map for use in target detection tasks. The experimental results show that the detection speed of the method proposed in this paper is 13.75% higher than that of SSD, and the garbage detection accuracy reaches 99.5%, which is better than the SSD detection algorithm. The garbage detection method proposed in this paper can quickly realize the precise positioning of garbage and complete automatic robot sorting.
Deep learning based on convolutional neural network (CNN) has attracted more and more attention in phase unwrapping of fringe projection three-dimensional (3D) measurement. However, due to the inherent limitations of convolutional operator, it is difficult to accurately determine the fringe order in wrapped phase patterns that rely on continuity and globality. To attack this problem, in this paper we develop a hybrid CNN-transformer model (Hformer) dedicated to phase unwrapping via fringe order prediction. The proposed Hformer model has a hybrid CNN-transformer architecture that is mainly composed of backbone, encoder, and decoder to take advantage of both CNN and transformer. Backbone is used as a wrapped phase pattern feature extractor. Encoder and decoder with cross attention are designed to enhance global dependency for the fringe order prediction. Experimental results show that the proposed Hformer model achieves better performance in fringe order prediction compared with the CNN models such as U-Net and DCNN. Our work opens an alternative way to the CNN-dominated deep learning phase unwrapping of fringe projection 3D measurement.
In view of the problem that traditional collaborative robots mostly work according to the prescribed path through teaching or offline programming cannot obtain the feedback information of the robot in real time, resulting in low intelligence. A multi-decision pressure compensation robot massage control system based on stereo vision imaging is proposed. It could reduce the execution time and positioning error of the robot when conducting human body positioning massage, and perform force compensation based on the massage pressure feedback in real time to improve the level of human-computer interaction. Firstly, hand-eye calibration is carried out through binocular vision sensor and collaborative robot, and the robot positioning control is realized by using high-precision real-time point cloud generated by structured light compensation. The massage pressure is fed back through the pressure sensor on the massage end which the massage force is trimmed to ensure the safety and comfort during the massage process. In the visual working range, the experimental data shows that the depth positioning error is within 0.277mm and the accuracy of the robot massage in the appropriate pressure range can reach 97.35%, which effectively solves the problems in robot massage and increases the standard of human-computer interaction and the immersive experience which is made innovations in the direction of stereo imaging technology and medical treatment.
Phase encoding and phase-shift profilometry are two commonly used 3D measurement techniques. However, the acquired phases in the techniques are subject to jump errors due to phase ambiguity and phase errors caused by multiple heterodyne. The phase-shifting profilometry also makes the selection of fringe period difficult. To overcome this problem and achieve high-precision measurement, a phase unwrapping method that combines dual-frequency heterodyne with double complementary phase encoding is proposed. First, two wrapped phases are obtained by two groups of sinusoidal fringes; the heterodyne phase is obtained after heterodyne processing, and the high-frequency phase is expanded by heterodyne phase. Second, the fringe levels are obtained using the complementary phase encoding fringes that are shifted by half an order, and then the absolute phase is obtained by selecting different phase coding levels according to different regions for the first phase unwrapping; Finally, the phase noise is removed by exploiting the difference between the phase slopes of adjacent pixels. Experimental results show that a system with the proposed method achieves an RMS error of 0.015 mm. In addition, the period of dual-frequency heterodyne synthesis does not need to cover the whole field of view, which breaks the limitation of frequency selection of the traditional dual-frequency heterodyne method and triple frequency heterodyne method, enabling high-precision measurement with higher frequency fringes. This method overcomes the limitations of the phase principal value error when using higher frequency fringes for high-precision measurement, improves the measurement effect of reflective objects, and effectively avoids the error caused by phase jump.
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