Infrared small-target detection plays a crucial role in applications such as public safety monitoring. However, it faces significant challenges due to the loss of target features, which weakens detection performance. To tackle this problem, this study proposes an improved infrared small-target detection model based on YOLOv8n. First, the Dual-Path Fusion Downsampling Convolution (WFDC) module enhances the backbone network’s ability to extract fine-grained features of targets, preventing the loss of image details as the depth of the convolutional neural network increases. Second, the Involution and the Coordinate Attention (CA) mechanisms are integrated into the spatial pyramid pooling module, where self-attention and the Involution modules aggregate contextual semantic information over a broader spatial range, enriching channel information at each scale. Finally, deformable convolutions are incorporated into the backbone of the model, enabling better handling of target deformations across various scenarios. Experiments conducted on the SIRST-5K and IRSTD-1K datasets demonstrate that our method outperforms both the baseline YOLOv8 model and several state-of-the-art YOLOv8-based improved detection methods. The results show that, compared to the baseline model, our approach achieves mAP@[0.5:0.95] improvements of 12.3% and 16.4% on the two datasets, respectively. These results highlight the effectiveness of our proposed enhancements in improving detection accuracy and model robustness.

The cooled mid-wave infrared biomimetic compound eye camera has wide range of applications, such as industrial inspection, military project, and security. Due to the low resolution of individual eyes and the large field view of the imaging system, existing motion target enhancement and detection algorithms cannot effectively detect all potential targets. To address this issue, we propose an improved elementary motion detector model that combines a double-layer ON_OFF channel and a cross-type computational architecture, which is able to suppress a stationary background and enhance moving targets. In order to further reduce the missed detection, we designed the spatial and temporal consistency detection methods of the compound eye structure, which further improved the accuracy and stability of the detection results. The experimental results show that our method can fully utilize the features of the image and can be applied to the enhancement and detection of moving objects in complex scenes, and the detection efficiency is significantly improved.

To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is experimentally evaluated. The compound-eye camera consists of a small-lens array with a spherical shell, a relay optical system, and a cooling-type mid-wave infrared detector. Based on the spatial arrangement of the small-lens array, a precise simulation imaging model for the compound-eye camera is developed, constructing a virtual imaging space. Distance estimation and error analysis for virtual targets are performed using the principle of stereo disparity. This universal simulation method provides a foundation for spatial design and image-plane adjustments for compound-eye cameras with specialized structures. Using the raw images captured by the compound-eye camera, a scene-specific piecewise linear mapping method is applied. This method significantly reduces the brightness contrast differences between sub-images during wide-field observations, enhancing image details. For the fast detection of moving targets, ommatidia clusters are defined as the minimal spatial constraint units. Local information at the centers of these constraint units is prioritized for processing. This approach replaces traditional global detection methods, improving the efficiency of subsequent processing. Finally, the simulated distance measurement results are validated using real-world scene data.

To address the issues of insufficient detector target size and high system complexity in infrared bionic compound-eye systems, this paper designs a miniaturized cooled medium-wave infrared curved bionic compound-eye optical system specifically for large target surface detectors and develops a proof-of-concept prototype for verification. The system comprises three components: (1) a curved multi-aperture array, which consists of 61 sub-apertures with an entrance pupil diameter of 5 mm and a focal length of 10 mm; (2) a cooled planar detector; and (3) a relay imaging system, which adopts secondary imaging technology and achieves the matching between the array and detector with only six infrared lenses. The fill factor is introduced to analyze light energy utilization efficiency, providing a theoretical basis for improving the system’s signal-to-noise ratio and spatial information collection capability; meanwhile, the focal length distribution and pupil matching are analyzed to ensure the system’s optical performance. The system operates within the 3.7–4.8 μm wavelength band, with a total focal length of 3.08 mm, F-number of 2, and field of view reaching 108°. Simulations demonstrate that all sub-aperture imaging channels have MTF values greater than 0.47 at 33.3 lp/mm, with distortion less than 3%. Imaging test results verify that the system possesses excellent imaging performance.

Dynamic street-scene reconstruction from sparse viewpoints over long temporal spans is challenged by temporal instability, ghosting near occlusions, and background drift. This paper presents SPT-Gauss (Semantic Prior and Temporal constraint-enhanced Gaussian splatting), a Gaussian-splatting framework that improves dynamic reconstruction without object-level annotations by combining dense semantic priors with lightweight, parameter-level temporal regularization. SPT-Gauss distills per-pixel semantic features from a frozen 2D foundation model into 4D Gaussian primitives, estimates static and dynamic regions via a dual-evidence motion mask, and regularizes temporal parameters through a semantic-guided velocity constraint and a static-lifetime prior to suppress spurious background motion. Experiments on the Waymo Open Dataset and KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) show consistent improvements over representative baselines in both 4D reconstruction and novel-view synthesis, with reduced temporal artifacts and improved fidelity in motion-challenging regions.

Dynamic street-scene reconstruction from sparse viewpoints over long temporal spans is challenged by temporal instability, ghosting near occlusions, and background drift. This paper presents SPT-Gauss, a Gaussian-splatting framework that improves dynamic reconstruction without object-level annotations by combining dense semantic priors with lightweight, parameter-level temporal regularization. SPT-Gauss distills per-pixel semantic features from a frozen 2D foundation model into 4D Gaussian primitives, estimates static and dynamic regions via a dual-evidence motion mask, and regularizes temporal parameters through a semantic-guided velocity constraint and a static-lifetime prior to suppress spurious background motion. Experiments on the Waymo Open Dataset and KITTI show consistent improvements over representative baselines in both 4D reconstruction and novel-view synthesis, with reduced temporal artifacts and improved fidelity in motion-challenging regions.