In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. This mechanism processes the downsampled feature maps, enabling the model to better focus on key regions. Secondly, to address the issue of insufficient feature fusion, we employ combined serial and parallel triple feature encoding (TFE). This approach fuses scale-sequence features from both shallow features and twice-encoded features, resulting in a new small-scale object detection layer. While enhancing the global context awareness of the existing detection layers, this also enriches the small-scale object detection layer with detailed information. Since rich redundant features often ensure a comprehensive understanding of the input, which is a key characteristic of deep neural networks, we propose a more efficient redundant feature generation module. This module generates more feature maps with fewer parameters. Additionally, we introduce reparameterization techniques to compensate for potential feature loss while further improving the model’s inference speed. Experimental results demonstrate that our proposed RTSOD-YOLO achieves superior detection performance, with ${\mathrm{mAP}}_{50}$/${\mathrm{mAP}}_{50:95}$ reaching 97.3%/51.7%, which represents improvement of 3%/3.5% over YOLOv8, and 2.6%/0.1% higher than YOLOv10. Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources.

We demonstrate a technique to realize fine target tracking and pointing using target-in-the-loop (TIL) concept via a coherent beam combining (CBC) system. A tracking and pointing system is composed of a densely packed array of 7 phase-control elements with simultaneous piston and tilt controls of the outgoing beams wavefront phases. A photo-detector (PD) with a pinhole is used as target detection and installed on a translation stage. Energy detected by the mobile PD is used for evaluating the beam combining and scanning performance, and to provide the feedback signal for controls of piston and tilt phases of the transmitted beams by using the stochastic parallel gradient descent (SPGD) algorithm for merit function maximization.

As next-generation large-aperture telescopes, synthetic aperture is a promising method for realizing high resolution observations. Co-phasing the misaligned segmented aperture is an important procedure for high-resolution observations with segmented telescopes. In this paper, a piston error detection method is proposed based on two interference patterns. Two interference patterns are generated by using a lens placed across two adjacent pupils in the exit pupil plane at two wavelengths and a method based on phase retrieval technique is proposed to extract the piston error from the two interference patterns. The introduction of dual-wavelength in the scheme overcomes the 2π ambiguities problem and expands the piston error detection range. Meanwhile, the proposed piston error extraction method based on phase retrieval technique allows high precision measurement of the piston error and is robust to offset lens. Various simulations are demonstrated and the feasibility of the proposed piston error detection method is validated.