Computed Tomography (CT) is currently the mainstream method for reconstructing bronchial environments. Although this technique can reveal the internal structure of the airways, it provides relatively limited information, leading to the neglect of many useful details. This paper proposes a novel bronchial environment semantic reconstruction algorithm that integrates geometric constraints with region growing. The aim is to address the limitations of traditional reconstruction methods, which often fail to capture sufficient airway detail. Clinically, the diameter of surgically relevant bronchial structures typically exceeds 2 mm, yet conventional CT-based reconstructions struggle to preserve fine features at this scale. By combining region growing with geometric constraint-based segmentation of the airway, our method achieves high-precision semantic bronchial modeling. Experimental results using ex vivo porcine lungs demonstrate that the preoperative semantic bronchial point cloud models reconstructed by our method provide surgeons with richer intraoperative information and achieve a reconstruction accuracy of up to 1 millimeter.
To address issues in underwater images, such as blurriness, low contrast, and color distortion, we propose a Generative Adversarial Network (GAN) based on a hybrid attention mechanism and Transformer, named HAT-GAN. HAT-GAN is designed to efficiently recover image details and improve visual quality. The proposed framework incorporates a Multi-Domain Attention Transformer, which processes query information in the frequency domain while handling key-value information in the spatial domain to generate attention maps with high information density. Additionally, in the up-sampling and down-sampling modules, different attention mechanisms are applied at different sampling stages to acquire richer information. Finally, in the discriminator, a Dual Attention mechanism is employed, enabling the discriminator to focus on critical regions of the image. Through experimental comparisons and ablation studies, we show that our approach successfully mitigates issues such as image detail blur and color distortion in underwater images, producing visually pleasing results.
Robotic welding is a critical component of industrial automation, with weld seam detection playing a vital role in ensuring both accuracy and efficiency in welding operations. While precise laser sensors and point cloud cameras are commonly employed to improve welding detection accuracy, current methods often fall short of meeting the speed requirements essential for industrial applications. This paper proposes an efficient multi-weld seams detection method that leverages CAD-guided image segmentation to achieve fast and accurate detection. Our approach employs computationally efficient image segmentation to detect weld seams, in contrast to conventional point cloud processing methods, which involve heavy computational overhead. The detected weld seams in image space are then mapped to the 3D point cloud, enabling the final robot-executable welding paths. Furthermore, to enhance detection accuracy, we introduce a background removal method guided by the CAD model of the workpiece, providing clean data for the image segmentation process. Extensive experiments conducted on the real-world robotic welding system validate the proposed method, demonstrating significant improvements in both accuracy and efficiency compared to existing state-of-the-art techniques.
In this study, we focused on the thoracic joint of a click beetle and proposed a two-link type snap mechanism. This mechanism mimics the click beetle's principle for storing and releasing elastic energy, and is composed of links, springs, motors, viscoelastic peg, etc. This mechanism normally functions as a general active joint, and can generate snap rotational motion at any time. The developed snap mechanism was likened to a click beetle and the relationships between the mechanism's parameter and jumping characteristics were validated. As a result, we confirmed that the size of the mechanism, weight distribution, and hardness of the ground affect the jumping performance.
Semantic segmentation of aerial images is vitally important to many aspects of autonomous driving. However, when applied to the segmentation of large-scale aerial imagery acquired from diverse geographic regions, pre-trained deep learning models often fail to produce consistently accurate predictions. To address this challenge, this paper introduces a novel unsupervised domain adaptation (UDA) method leveraging entropy-driven attention for the semantic segmentation of aerial images. The entropy-driven attention strategy contains a two-stage adversarial learning process, which utilizes the entropy map to explicitly measure the data distribution distance between two domains and then guides the model to focus on the poorly aligned features. We conducted extensive experiments on the LoveDA dataset to validate the effectiveness of our proposed UDA method. The quantitative results indicate that our approach surpasses six other state-of-the-art UDA methods, achieving superior performance.
