Objective . In this study, we propose a model called DEPMSCNet (a multiscale self-calibration network) that has a high sensitivity and low false positive rate for detecting pulmonary nodules. Approach . First, at the feature extraction stage, we propose to use the REPSA-MSC module instead of the traditional convolutional neural network. The module extracts multiscale information from the feature map based on the image pyramid strategy while introducing adaptive convolutional branches to detect contextual information at each position of the multiscale, thereby expanding the receptive field and improving sensitivity. At the same time, multiple branches are adaptively weighted by channel attention, and the weights of different branches are adjusted to better generate pixel-level attention. Secondly, the proposed DSAM (dual-path spatial attention module) operates at the information fusion stage. This module fully exploits the rich spatial information of CT scans, obtains receptive field information from two branches, combines low-level feature map information with high-level semantic information, and enhances location-related information to effectively improve specificity. Thirdly, the focal loss function is used to solve the problem of positive and negative sample imbalance. Main results . The proposed model has been evaluated on the public lung nodule analysis (LUNA16) challenge dataset. The technique outperforms the most recent state-of-the-art detection algorithms in terms of sensitivity and specificity, obtaining a sensitivity of 0.988 and a competitive performance metric (CPM) of 0.963. Significance . Ablation experiments show that the two modules proposed in this paper effectively reduce false positives and improve sensitivity. This model effectively reduces the number of false positive nodules that doctors see on CT scans.

This paper addresses the objectives in a complex context: the polarization mathematical and physical model of the basic components of the target/background is constructed by simulation modeling. The image fusion experiment is carried out based on a two-dimensional wavelet transformation. A high-resolution polarization imaging instrument was developed, and static experiments were carried out with polarization, infrared, and visible cameras. The result shows that using polarization imaging detection technology to detect physical evidence targets in complex backgrounds can effectively improve target contrast. It can enrich the target information, improve image quality, and improve target detection accuracy.

With the increasing demand for precise identification of underwater targets, the development of advanced underwater detection technologies has become a pivotal area of research. This study presents the design and implementation of a laser-based underwater detection system that leverages polarization characteristics to significantly enhance detection accuracy and target identification capabilities in complex aquatic environments. A key innovation of this research lies in the application of a dual-frequency modulation technology using a 532 nm pulsed laser. By modulating the high-frequency characteristics of the laser, this technique effectively suppresses backscattering interference within the water medium, improves the efficiency of target signal extraction, and exhibits exceptional performance, particularly in long-range detection and highly turbid water conditions. This paper elucidates the principles of underwater laser detection and polarization measurement, outlines the design of an integrated optical and mechanical system for laser transmission and reception, and introduces an optimized signal processing methodology. Experimental results demonstrate that the proposed system can reliably distinguish targets composed of different materials while maintaining high detection accuracy across a range of challenging environmental conditions. A comparative analysis further highlights the system’s significant advantages over traditional technologies, including enhanced noise suppression and greater detection depth. These findings establish a solid foundation for advancing underwater detection technologies and broadening their practical applications.

There are six possible solutions for the surface normal vectors obtained from polarization information during 3D reconstruction. To resolve the ambiguity of surface normal vectors, scholars have introduced additional information, such as shading information. However, this makes the 3D reconstruction task too burdensome. Therefore, in order to make the 3D reconstruction more generally applicable, this paper proposes a complete framework to reconstruct the surface of an object using only polarized images. To solve the ambiguity problem of surface normal vectors, a jump-compensated U-shaped generative adversarial network (RU-Gan) based on jump compensation is designed for fusing six surface normal vectors. Among them, jump compensation is proposed in the encoder and decoder parts, and the content loss function is reconstructed, among other approaches. For the problem that the reflective region of the original image will cause the estimated normal vector to deviate from the true normal vector, a specular reflection model is proposed to optimize the dataset, thus reducing the reflective region. Experiments show that the estimated normal vector obtained in this paper improves the accuracy by about 20° compared with the previous conventional work, and improves the accuracy by about 1.5° compared with the recent neural network model, which means the neural network model proposed in this paper is more suitable for the normal vector estimation task. Furthermore, the object surface reconstruction framework proposed in this paper has the characteristics of simple implementation conditions and high accuracy of reconstructed texture.