Aiming at the approach and landing of an aircraft under low visibility, this paper studies the use of an infrared heat-transfer imaging camera and visible-light camera to obtain dynamic hyperspectral images of flight approach scenes from the perspective of enhancing pilot vision. Aiming at the problems of affine deformation, difficulty in extracting similar geometric features, thermal shadows, light shadows, and other issues in heterogenous infrared and visible-light image registration, a multi-modal image registration method based on RoI driving in a virtual scene, RoI feature extraction, and virtual-reality-fusion-based contour angle orientation is proposed, and this could reduce the area to be registered, reduces the amount of computation, and improves the real-time registration accuracy. Aiming at the differences in multi-modal image fusion in terms of resolution, contrast, color channel, color information strength, and other aspects, the contour angle orientation maintains the geometric deformation of multi-source images well, and the virtual reality fusion technology effectively deletes incorrectly matched point pairs. By integrating redundant information and complementary information from multi-modal images, the visual perception abilities of pilots during the approach process are enhanced as a whole.

As a fundamental and indispensable component of modern electronic devices, the printed circuit board (PCB) has a complex structure and highly integrated functions, with its manufacturing quality directly affecting the stability and reliability of electronic products. However, during large-scale automated PCB production, its surfaces are prone to various defects and imperfections due to uncontrollable factors, such as diverse manufacturing processes, stringent machining precision requirements, and complex production environments, which not only compromise product functionality but also pose potential safety hazards. At present, PCB defect detection in industry still predominantly relies on manual visual inspection, the efficiency and accuracy of which fall short of the automation and intelligence demands in modern electronics manufacturing. To address this issue, in this paper, we have made improvements based on the classical Faster-RCNN object detection framework. Firstly, ResNet-101 is employed to replace the conventional VGG-16 backbone, thereby enhancing the ability to perceive small objects and complex texture features. Then, we extract features from images by using deformable convolution in the backbone network to improve the model’s adaptive modeling capability for deformed objects and irregular defect regions. Finally, the Convolutional Block Attention Module is incorporated into the backbone, leveraging joint spatial and channel attention mechanisms to improve the effectiveness and discriminative power of feature representations. The experimental results demonstrate that the improved model achieves a 4.5% increase in mean average precision compared with the original Faster-RCNN. Moreover, the proposed method exhibits superior detection accuracy, robustness, and adaptability compared with mainstream object detection models, indicating strong potential for engineering applications and industrial deployment.

In the current field of air traffic control speech, there is a lack of effective objective speech quality evaluation methods. This paper proposes a new network framework based on ResNet–BiLSTM to address this issue. Firstly, the mel-spectrogram of the speech signal is segmented using the sliding window technique. Next, a preceding feature extractor composed of convolutional and pooling layers is employed to extract shallow features from the mel-spectrogram segment. Then, ResNet is utilized to extract spatial features from the shallow features, while BiLSTM is used to extract temporal features, and these features are horizontally concatenated. Finally, based on the concatenated spatiotemporal features, the final speech quality score is computed using fully connected layers. We conduct experiments on the air traffic control speech database and compare the objective scoring results with the subjective scoring results. The experimental results demonstrate that the proposed method has a high correlation with the mean opinion score (MOS) of air traffic control speech.

Intelligent waveform analysis surveying geological structures is a challenging task in remote acoustic measurement for formation detection, which has two problems: (1) time parametric imaging is disturbed by noisy environments and (2) manually annotated data for machine learning are unattainable. These restrict the deployment of advanced parameter extraction methods in imaging instruments. As a potential theory, domain adaptation makes the intelligent prediction implementable in the above situations. Hence, to counter high precision travel time extraction for acoustic imaging, a deep adaptation‐picking network (DAPN) framework is proposed, which consists of three modules: signal‐to‐signal generator, domain adaptation encoder, and feature recognition decoder. The translation module preprocesses the different datasets to improve the training accuracy and confuses the distribution between source and target domains based on the adversarial network. The core convolution neural network achieves travel time measurement, where the backbone extracts content features with modified maximum mean discrepancy embedding. Meanwhile, the decoding structure preserves the signal features in the training process. The effectiveness and feasibility of DAPN are verified by experimental results, suggesting that DAPN accurately extracts time parameters. Compared with traditional parametric imaging schemes, the proposed method has the advantages of high accuracy and anti‐noise ability.

The fast compressed sensing (CS) reconstruction for natural image processing aims to infer the original pixel vector from randomized measurements as correctly as possible. Current first-order proximal mainstream schemes, including alternating direction methods of multipliers (ADMM) and the iterative shrinkage thresholding algorithm (ISTA), have advantages in convergence and stability at the expense of computing speed and complex tuning strategy. This paper proposes the deep iterative shrinkage network for real-time applications, where network layers replace the iterative optimization steps. The hyperparameters associated with shrinkage thresholds and step sizes are adjusted by standard deep learning updates. Numerical results suggest that the proposed method has flexibility and effectiveness in image reconstruction with different compression ratios while preserving the computational simplicity of end-to-end methods.