Automation

Head posture assessment commonly involves measuring cranial angles, with photogrammetry favored for its simplicity over CT scans or goniometers. However, most photo-based measurements remain manual, making them time-consuming and inefficient. Existing automatic measuring approaches often requires specific markers and clean backgrounds, limiting their usability. We present AutoMCA, a robust automatic measurement system for cranial angles using accessible markers and tolerating typical indoor backgrounds. AutoMCA integrates MediaPipe Pose, a machine-learning solution, for head–neck segmentation and applies color thresholding and morphological operations for marker detection. Validation tests demonstrated Pearson correlation coefficients above 0.98 compared to manual Kinovea measurements for both the craniovertebral angle (CVA) and cranial rotation angle (CRA), confirming high accuracy. Further validation on individuals with neck disorders showed similarly strong correlations, supporting clinical applicability. Speed comparison tests revealed that AutoMCA significantly reduces measurement time compared to traditional photogrammetry. Robustness tests confirmed reliable performance across varied backgrounds and marker types. In conclusion, AutoMCA measures head posture efficiency and lowers the requirements for instruments and space, making the assessment more versatile and applicable.

A single drone collects data, but a fleet builds a complete picture, and this is the primary objective of this study. To address this goal, a swarm-based drone system has been designed in which multiple drones follow one another to collect data from diverse perspectives. Such a strategy demonstrates strong potential for use in critical fields such as search and rescue operations. This study introduces the first unified framework that integrates autonomous formation control, real-time object detection, and multi-source data fusion within a single operational UAV-swarm system. A high-fidelity simulation environment was built using Unreal Engine with the AirSim plugin, featuring a lightweight QR code tracking algorithm for inter-drone coordination. The drones were employed to detect vehicles from various angles in real time. Two types of experiments were conducted: the first used a pretrained YOLO model, and the second used a custom-trained YOLOv8-nano model, which outperformed the baseline by achieving an average detection confidence of 90%. Finally, the results from multiple drones were fused using various techniques including temporal, probabilistic, and geometric fusion methods to produce more reliable and robust detection results.

Infrared small target detection has become a research hotspot in recent years. Due to the small target size and low contrast with the background, it remains a highly challenging task. Existing infrared small target detection algorithms are generally implemented on 8-bit low dynamic range (LDR) images, whereas raw infrared sensing images typically possess a 14–16 bit high dynamic range (HDR). Conventional HDR image enhancement methods do not consider the subsequent detection task. As a result, the enhanced LDR images often suffer from overexposure, increased noise levels with higher contrast, and target distortion or loss. Consequently, discriminative features in HDR images that are beneficial for detection are not effectively exploited, which further increases the difficulty of small target detection. To extract target features under these conditions, existing detection algorithms usually rely on large parameter models, leading to an unsatisfactory trade-off between efficiency and accuracy. To address these issues, this paper proposes a novel infrared small target detection framework based on HDR image enhancement (HDR-IRSTD). Specifically, a multi-branch feature extraction and fusion mapping subnetwork (MFEF-Net) is designed to achieve the mapping from HDR to LDR. This subnetwork effectively enhances small targets and suppresses noise while preserving both detailed features and global information. Furthermore, considering the characteristics of infrared small targets, an asymmetric Vision Mamba U-Net with multi-level inputs (AVM-Unet) is developed, which captures contextual information effectively while maintaining linear computational complexity. During training, a bilevel optimization strategy is adopted to collaboratively optimize the two subnetworks, thereby yielding optimal parameters for both HDR infrared image enhancement and small target detection. Experimental results demonstrate that the proposed method achieves visually favorable enhancement and high-precision detection, with strong generalization ability and robustness. The performance and efficiency of the method exhibit a well-balanced trade-off.