Convolutional neural network (CNN)-based object detectors perform excellently but lack global feature extraction and cannot establish global dependencies between object pixels. Although the Transformer is able to compensate for this, it does not incorporate the advantages of convolution, which results in insufficient information being obtained about the details of local features, as well as slow speed and large computational parameters. In addition, Feature Pyramid Network (FPN) lacks information interaction across layers, which can reduce the acquisition of feature context information. To solve the above problems, this paper proposes a CNN-based anchor-free object detector that combines transformer global and local feature extraction (GLFT) to enhance the extraction of semantic information from images. First, the segmented channel extraction feature attention (SCEFA) module was designed to improve the extraction of local multiscale channel features from the model and enhance the discrimination of pixels in the object region. Second, the aggregated feature hybrid transformer (AFHTrans) module combined with convolution is designed to enhance the extraction of global and local feature information from the model and to establish the dependency of the pixels of distant objects. This approach compensates for the shortcomings of the FPN by means of multilayer information aggregation transmission. Compared with a transformer, these methods have obvious advantages. Finally, the feature extraction head (FE-Head) was designed to extract full-text information based on the features of different tasks. An accuracy of 47.0% and 82.76% was achieved on the COCO2017 and PASCAL VOC2007 + 2012 datasets, respectively, and the experimental results validate the effectiveness of our method.

Multi-scale representation provides an effective answer to the scale variation of objects and entities in semantic segmentation. The ability to capture long-range pixel dependency facilitates semantic segmentation. In addition, semantic segmentation necessitates the effective use of pixel-to-pixel similarity in the channel direction to enhance pixel areas. By reviewing the characteristics of earlier successful segmentation models, we discover a number of crucial elements that enhance segmentation model performance, including a robust encoder structure, multi-scale interactions, attention mechanisms, and a robust decoder structure. The attention mechanism of the asymmetric non-local neural network (ANNet) is merged with multi-scale pyramidal modules to accelerate model segmentation while maintaining high accuracy. However, ANNet does not account for the similarity between pixels in the feature map channel direction, making the segmentation accuracy unsatisfactory. As a result, we propose EMSNet, a straightforward convolutional network architecture for semantic segmentation that consists of Integration of enhanced regional module (IERM) and Multi-scale convolution module (MSCM). The IERM module generates weights using four or five-stage feature maps, then fuses the input features with the weights and uses more computation. The similarity of the channel direction feature graphs is also calculated using ANNet’s auxiliary loss function. The MSCM module can more accurately describe the interactions between various channels, capture the interdependencies between feature pixels, and capture the multi-scale context. Experiments prove that we perform well in tests using the benchmark dataset. On Cityscapes test data, we get 82.2% segmentation accuracy. The mIoU in the ADE20k and Pascal VOC datasets are, respectively, 45.58% and 85.46%.

Lead-free perovskites of Cs₃Sb₂X₉ (X = Cl, Br, or I) have attracted wide attention owing to their low toxicity. High pressure is an effective and reversible method to tune bandgap without changing the chemical composition. Here, the structural and photoelectric properties of Cs₃Sb₂X₉ under high pressure were theoretically studied by using the density functional theory. The results showed that the ideal bandgap for Cs₃Sb₂X₉ can be achieved by applying high pressure. Moreover, it was found that the change of the bandgap is caused by the shrinkage of the Sb-X long bond in the [Sb₂X₉]³⁻ polyhedra. Partial density of states indicated that Sb-5s and X-p orbitals contribute to the top of the valence band, while Sb-5p and X-p orbitals dominate the bottom of the conduction band. Moreover, the band structure and density of states showed significant metallicity at 38.75, 24.05 GPa for Cs₃Sb₂Br₉ and Cs₃Sb₂I₉, respectively. Moreover, the absorption spectra showed the absorption edge redshifted, and the absorption coefficient of the Cs₃Sb₂X₉ increased under high pressure. According to our calculated results, the narrow bandgap and enhanced absorption ability under high pressure provide a new idea for the design of the photovoltaic and photoelectric devices.

We study the propagation characteristics (spectral intensity and degree of coherence) of a new type of Lorentz non-uniformly correlated (LNUC) beam based on the extended Huygens–Fresnel principle and the spatial power spectrum of oceanic turbulence. The effects of the oceanic turbulence parameters and initial beam parameters on the evolution propagation characteristics of LNUC beams are studied in detail by numerical simulation. The results indicate that such beams exhibit self-focusing propagation features in both free space and oceanic turbulence. Decreasing the dissipation rate of kinetic energy per unit mass of fluid and the Kolmogorov inner scale, or increasing the relative strength of temperature to salinity undulations and the dissipation rate of mean-square temperature of the turbulent ocean tends to increase the negative effects on the beams. Furthermore, we propose a strategy of increasing the beam width and decreasing the coherence length, to reduce the negative effects of the turbulence.

Purpose: Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach: This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value: This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.