Low-altitude Unmanned Aerial Vehicle (UAV) detection using LiDAR range images faces persistent challenges. These include sparse features for long-range targets, large scale variations caused by viewpoint changes, and severe interference from complex backgrounds. To address these issues, we propose an improved detection framework based on YOLOv10. First, we design a Swin-Conv hybrid module that combines sparse attention with deformable convolution. This module enables the network to focus on informative regions and adapt to target geometry. These capabilities jointly strengthen feature extraction for sparse, long-range targets. Second, we introduce Attentional Feature Fusion (AFF) in the neck to replace naïve feature concatenation. AFF employs multi-scale channel attention to softly select and adaptively weight features from different levels, improving robustness to multi-scale targets. In addition, we systematically study how the viewpoint distribution in the training set affects performance. The results show that moderately increasing the proportion of low-elevation-view samples significantly improves detection accuracy. Experiments on a self-built simulated LiDAR range-image dataset demonstrate that our method achieves 88.96% mAP at 54.2 FPS, which is 4.78 percentage points higher than the baseline. Deployment on the Jetson Orin Nano edge device further validates the model’s potential for real-time applications. The proposed method remains robust under noise and complex backgrounds. The proposed approach achieves an effective balance between detection accuracy and computational efficiency, providing a reliable solution for real-time target detection in complex low-altitude environments.

With the rapid advancement of biology and life sciences, there is an increasing demand for observing sub-cellular structures and molecular interactions at submicroscopic or even single-molecule levels, providing critical insights into life activities and disease diagnostics. Raman spectroscopy, which relies on molecular vibrational energy transitions, enables non-label and non-invasive cellular visualization, holding significant potential for modern medical technology. The microscopy method based on the coherent anti-Stokes Raman scattering effect, a novel visualization modality with superior signal intensity, chemical specificity, and label-free capability, demonstrates great promise in biomedical applications. Recently, dual-comb technology, consisting of two frequency combs with slightly different repetition rates, as a powerful light source has been successfully applied in CARS applications with the excellent performance characteristics of rapid acquisition, high resolution, and high signal-to-noise ratio. The dual-comb technique allows to clearly resolve sharp molecular lines and could suppress the non-resonant background in CARS. Through recent research progress, this work reviews the generation of dual-comb lasers based on a single cavity, the development of dual-comb CARS systems, and their biomedical applications. This review could provide further insights into high-resolution dual-comb CARS and potential ways to design such technology for potential biomedical applications.

Infrared small-target detection is commonly implemented as binary pixel-level segmentation and localization but remains challenging because targets occupy very few pixels and are embedded in complex backgrounds. This paper develops the Semantic-Guided Enhanced UIU-Net (SGUIU-Net) as a task-specific extension of U-Net in U-Net (UIU-Net) by combining Dilated Residual U-blocks (RSUs), the Convolutional Block Attention Module (CBAM), Focal–Dice deep supervision, and Semantic-Guided Cross-Attention Fusion (SGCA-Fusion). Experiments are conducted on the 427-image SIRST benchmark and the auxiliary IRSTD-1K and SIRST-V2 datasets. On SIRST, the full model obtains an IoU of 0.7995 ± 0.0011 and an nIoU of 0.7572 ± 0.0012 over three independent runs, improving the matched UIU-Net baseline means by 0.0180 and 0.0057, respectively. The model also improves IoU/nIoU from 0.6379/0.6057 to 0.7125/0.6787 on IRSTD-1K and from 0.7215/0.6832 to 0.7364/0.6937 on SIRST-V2. These gains are accompanied by an increase from 50.54 M to 106.31 M parameters and a decrease from 50.05 to 32.53 FPS at 320 × 320 resolution.

To address the challenges of insufficient feature representation and the difficulty of detecting sparse and distant objects in UAV-borne LiDAR point clouds—which exhibit significantly lower point density than terrestrial/mobile LiDAR scans—this paper proposes an enhanced detection algorithm built upon the PointPillars framework. First, a coordinate attention mechanism is incorporated to enhance the network’s ability to capture spatial geometric information. Furthermore, the backbone network is redesigned with a dual-path structure and a feature modulation fusion module, enabling adaptive integration of multi-scale features. Experimental evaluations conducted on a custom simulated UAV-borne LiDAR point cloud dataset demonstrate that the proposed method achieves 85.35% 3D mAP and 88.26% BEV mAP, corresponding to absolute improvements of 21.61 and 7.85 percentage points compared with the original PointPillars model. In addition, the proposed approach demonstrates consistent performance on the publicly available KITTI benchmark through preliminary cross-dataset validation. The results indicate that the proposed method can effectively improve the detection accuracy and robustness of LiDAR-based 3D object detection in complex environments.

To improve depth reconstruction accuracy of streak tube imaging LiDAR (STIL) under weak echo and low-contrast conditions in complex scattering environments, this paper proposes a hierarchical reliability-guided multispectral polarization reconstruction framework (MSP-STIL). The proposed method addresses measurement uncertainty in multi-wavelength and multi-polarization observations by constructing a progressive reliability modeling strategy, which evolves from polarization stability to statistical uncertainty and finally to signal strength enhancement. First, a Dual-channel Polarization Contrast (Pc) is introduced to evaluate local scattering stability and suppress fringe peak degradation caused by polarization distortion. Second, SNR is employed to model the uncertainty of depth measurements across different wavelength channels. Finally, echo intensity is incorporated as a confidence refinement factor to further enhance high-quality signals. Based on this hierarchical modeling strategy, an adaptive inverse-variance weighting scheme is developed to achieve robust multi-wavelength depth fusion. The results show that the proposed method outperforms equal-weight and single-feature methods in all test regions. Compared with the non-weighted method, the MAE, RE, MSE, RMSE, and STD are reduced by approximately 10.95%, 12.02%, 25.44%, 13.68%, and 15.58% on average, respectively. Under low signal-to-noise conditions (simulated by controlled noise levels) and long-distance detection scenarios, the proposed method still maintains low reconstruction errors and effectively suppresses depth fluctuations, demonstrating good noise resistance and distance robustness. In addition, the maximum contrast of the RGB image constructed through weighted fusion increases from 5.0065 to 5.8300, verifying the effectiveness of the proposed method in low-contrast complex scenes. Overall, the proposed MSP-STIL multi-feature joint weighting method shows clear advantages in reconstruction accuracy, robustness, and scene adaptability.