Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face; the face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.
Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application's requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high-and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.
The flow and heat transfer characteristics of nanofluids in a square cavity were simulated using single-phase and mixed-phase flow models, and the simulation results were compared with the corresponding experimental values. The effects of different prediction models for the thermal properties of nanofluids, Grashof number, and volume fraction on the Nusselt number were analysed. The velocity and temperature distributions of the nanofluid and deionised water in the square cavity were compared, and the effects of the temperature and flow fields on the enhanced heat transfer were analysed according to the field synergy theory. The results show that for the numerical simulation of convective heat transfer in water, both the single-phase flow models and multi-phase flow mixing models had high prediction accuracy. For nanofluids, single-phase flow did not reflect the heat transfer characteristics well, and the simulation results of the single-phase flow model relied more strongly on a highly accurate prediction model for the physical parameters. The multi-phase flow mixing model could better reflect the natural convective heat transfer properties of the nanofluids in a the square cavity. The nanofluid could significantly improve the flow state in the square cavity, thereby facilitating enhanced convective heat transfer. When the concentration is 2% (Grashof number is 1×106), the average Nusselt number of the nanofluid is increased by 19.7% compared with the base fluid.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.