During the reconstruction process, the measured object is affected by its own reflection, environmental illumination, and other factors, resulting in a large number of mismatching points during the stereo-matching process, thus affecting the accuracy of the three-dimensional reconstruction. Aiming at the above problems, this paper adopts a scanning method combining single-line laser and binocular vision. The binocular epipolar constraint is used to obtain the initial matching points, and the optical plane constraint is added to eliminate the false matching points. The experimental results show that the 3D modeling error of the workpiece is maintained within 0.5cm, which verifies that the method has certain feasibility and good accuracy in solving problems such as mismatching.
Background: At present, artificial intelligence has been widely used in fields such as facial recognition. Since facial expression is the most direct feature of emotion, it can be used to predict the emotional state of police dogs by recognizing facial expression. In our study, we developed and experimentally analyzed an improved method to predict the emotions of police dogs through deep learning to recognize their expressions. Methods: We sifted through and analyzed public dog data sets on the Internet. Finally, it was decided to use the dog data set published by Stanford University, and the categories of dogs were accurately divided and marked manually. Emotion recognition and experimental analysis of police dogs were carried out by using group recombination residual network. Results: Through experimental analysis and comparison with existing methods, the accuracy of this method is 6% higher than that of the method based on residual network, and the accuracy is up to 89%. In addition, the number and size of the model parameters are reduced. Conclusion: Through experimental analysis and comparison with other mainstream identification networks, this method has a good advantage in the number of network parameters. Compared with other methods, the residual network based on group recombination can significantly improve the accuracy of emotion recognition for police dogs.
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