Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner keypoints, and offset vectors relative to the center of the 2D bounding boxes, to enhance the accuracy of 3D object bounding box detection. Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv7-3D network achieved significantly higher detection accuracy on the Rope3D dataset while reducing computational complexity by 60%.

In this paper, research was conducted on Deep Learning Wavefront Sensing (DLWS) neural networks using simulated atmospheric turbulence datasets, and a novel DLWS was proposed based on attention mechanisms and Convolutional Neural Networks (CNNs). The study encompassed both indoor experiments and kilometer-range laser transmission experiments employing DLWS. In terms of indoor experiments, data were collected and training was performed on the platform built by us. Subsequent comparative experiments with the Shack-Hartmann Wavefront Sensing (SHWS) method revealed that our DLWS model achieved accuracy on par with SHWS. For the kilometer-scale experiments, we directly applied the DLWS model obtained from the indoor platform, eliminating the need for new data collection or additional training. The DLWS predicts the wavefront from the beacon light PSF in real time and then uses it for aberration correction of the emitted laser. The results demonstrate a substantial improvement in the average peak intensity of the light spot at the target position after closed-loop correction, with a remarkable increase of 5.35 times compared to the open-loop configuration.

Simulating atmospheric turbulence phase screens is essential for optical system research and turbulence compensation. Traditional methods, such as multi-harmonic power spectrum inversion and Zernike polynomial fitting, often suffer from sampling errors and limited diversity. To overcome these challenges, this paper proposes an improved denoising diffusion probabilistic model (DDPM) for generating high-fidelity atmospheric turbulence phase screens. The model effectively captures the statistical distribution of turbulence phase screens using small training datasets. A refined loss function incorporating the structure function enhances accuracy. Additionally, a self-attention module strengthens the model’s ability to learn phase screen features. The experimental results demonstrate that the proposed approach significantly reduces the Fréchet Inception Distance (FID) from 154.45 to 59.80, with the mean loss stabilizing around 0.1 after 50,000 iterations. The generated phase screens exhibit high precision and diversity, providing an efficient and adaptable solution for atmospheric turbulence simulation.

This study addresses the modeling and simulation challenges of urban laser countermeasure systems against Low-Slow-Small (LSS) UAVs by proposing a physics simulation framework integrating Geographic Information System (GIS)-based dynamic 3D real-world scenes and constructing a hybrid Anti-UAV dataset combining real and simulated data. A three-stage target tracking system is developed, encompassing target acquisition, coarse tracking, and precise tracking. Furthermore, the UAV-D-Fine detection algorithm is introduced, significantly improving small-target detection accuracy and efficiency. The simulation platform achieves dynamic fusion between target models and GIS real-scene models, enabling a full physical simulation of UAV takeoff, tracking, aiming, and laser engagement, with additional validation of laser antenna tracking performance. Experimental results demonstrate the superior performance of the proposed algorithm in both simulated and real-world environments, ensuring accurate UAV detection and sustained tracking, thereby providing robust support for low-altitude UAV laser countermeasure missions.

Wavefront sensing is an essential technique in optical imaging, adaptive optics, and atmospheric turbulence correction. Traditional wavefront reconstruction methods, including the Gerchberg–Saxton (GS) algorithm and phase diversity (PD) techniques, are often limited by issues such as low inversion accuracy, slow convergence, and the presence of multiple possible solutions. Recent developments in deep learning have led to new methods, although conventional CNN-based models still face challenges in effectively capturing global context. To overcome these limitations, we propose a Transformer-based single-shot wavefront sensing method, which directly reconstructs wavefront aberrations from focal plane intensity images. Our model integrates a Normalization-based Attention Module (NAM) into the CoAtNet architecture, which strengthens feature extraction and leads to more accurate wavefront characterization. Experimental results in both simulated and real-world conditions indicate that our method achieves a 4.5% reduction in normalized wavefront error (NWE) compared to ResNet34, suggesting improved performance over conventional deep learning models. Additionally, by leveraging Walsh function modulation, our approach resolves the multiple-solution problem inherent in phase retrieval techniques. The proposed model achieves high accuracy, fast convergence, and simplicity in implementation, making it a promising solution for wavefront sensing applications.