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image enhancement model for ultrasonic nondestructive testing based on deep learning super-resolution 12

Image enhancement model for ultrasonic nondestructive testin...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhu, Jinxuan Zhang, Xinfang He, Ruixian Kang, Luo Wang, Guoyou Huang, Yan School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Ultrasonic nondestructive testing has been widely used in industry due to its various advantages. However. with the increasing demand for non-destructive testing accuracy, low-resolution ultrasonic images can easily lead to misidentification of defects. How to improve the resolution of ultrasonic images has become a key issue restricting development of the non-destructive testing industry. Therefore. this paper proposes a super-resolution model for ultrasonic NDT images consisting of up and down sampling layers and deep residual networks. The model learns the degraded features of the image through the up and down sampling layers, and learns the intrinsic features of the image through the deep residual network, so as to learn the feature information of the image more completely to complete the super-resolution task of the image. Experimental results on ultrasound image datasets also validate the effectiveness of the model, outperforming other models in peak signa-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. © 2024 SPIE.

关键词： Nondestructive examination

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Optimal exposure selection of high dynamic range-based reflection compensation for printed circuit board reconstruction using structured light 3D measurement system 12

Optimal exposure selection of high dynamic range-based refle...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Wang, He Zhu, Ying Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Structured light (SL) scanning in industrial metrology offers the advantages of non-contact measurement, high-speed operation, and high precision. However, fringes may exhibit excessive modulation when measuring highly reflective metal parts, leading to the calculation of invalid phases and the reconstruction of abnormal point clouds. High dynamic range (HDR) scanning is a commonly used approach to address this issue, but the optimal exposure time using this method is usually influenced by the reflective properties of the object. In this paper, we propose an optimal exposure time selection method based on the clustering of images in the fringe modulation. Firstly, we acquire modulation degree images containing only objects. Subsequently, the K-Means method is employed to cluster the modulation degree images. The K best exposure times are calculated based on the average maximum gray value of the fringe images in each cluster. Finally, K sets of fused fringe images are captured and utilized to reconstruct three-dimensional (3D) information with enhanced coverage. Using our method, the three-dimensional information of the printed circuit board (PCB) is reconstructed, achieving an improved coverage rate of 98.1% compared to the 88.3% coverage rate obtained from a single-shot capture. Additionally, our method achieves a measurement accuracy of 0.08mm. Experimental results demonstrate that the proposed method efficiently selects the optimal fusion exposure time, making it suitable for measuring highly reflective PCBs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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third international Conference on image processing and Intelligent Control, IPIC 2023

Third International Conference on Image Processing and Intel...

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3rd international Conference on image processing and Intelligent Control, IPIC 2023

ISBN: (纸本)9781510668140

The proceedings contain 54 papers. The topics discussed include: image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems;a method of extracting outer eye corners of terra cotta warriors based on point cloud data;research on UI design and optimization of digital media with artificial intelligence;video description method with fusion of instance-aware temporal features;a multi-modal information fusion-based method for repairing cracks in train hooks;research on optical detection technology for underwater archaeology;discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis;numerical analysis and calculation of urban landscape spatial pattern;and sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image.

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An iterative attention context fusion network for semantic segmentation of remote sensing image 12

An iterative attention context fusion network for semantic s...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Chen, Yanpu Wang, Haihui Zhang, Shuiping Hu, Po Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively. © 2024 SPIE.

关键词： Semantics

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Design of polarimetric camera based on the single-layer twisted nematic liquid crystal device 12

Design of polarimetric camera based on the single-layer twis...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Ye, Mao Zhu, Wenjie Liu, Kewei Zhang, Xinyu Institude of Artificial Intelligence Huazhong University of Science and Technology Hubei Wuhan430074 China National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Polarization is the asymmetry of a light wave's electric field vibration direction with respect to its propagation direction, as a crucial clue for describing the light, which is one of the most apparent signs that distinguishes transverse waves from longitudinal waves. The current mainstream polarimetric cameras typically adopt a division of focal plane imaging architecture, in which a metal wire-grid structure is coupled with a detector to acquire the polarization state of the target. However, the structure of the division of focal plane reduces effective spatial resolution of the detector and requires complex registration algorithms to achieve pixel-level polarization acquisition, which leads to high equipment costs and impedes the development and application of polarimetric cameras in industrial production. In this paper, a single-layer twisted nematic liquid crystal (TNLC) is inserted to the imaging optical path of traditional camera to realize the polarization acquisition, allowing for a cost-effective transition from traditional devices to polarization imaging systems. The imaging detector coupled with the TNLC device can acquire polarization states ranging from 10 to 90-degree by adjusting the external voltage applied on the LC device. Moreover, a polarization angle calibration method based on a single-layer TNLC is proposed in this paper, providing a reliable reference for electrically controlled polarization angle acquisition. The experimental results acquired by presented imaging system demonstrate the feasibility and effectiveness of the proposed method. The TNLC based imaging scheme presented in this paper provides a solid foundation for developing simple, low-cost, and efficient polarimetric cameras. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cameras

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An Edge-Guided Network for Building Footprint Extraction from Optical Remote Sensing images 12

An Edge-Guided Network for Building Footprint Extraction fro...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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An attention supervision transformer full-resolution residual network for space satellite image segmentation 12

An attention supervision transformer full-resolution residua...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Wei, Yihang Fan, Shangchun Zhou, Jiale Hou, Zuoxun Zheng, Dezhi Wang, Shuai Qu, Xiaolei School of Instrumentation and Optoelectronics Engineering Beihang University Beijing100191 China Beijing Institute of Mechanics & Electricity Beijing100094 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

The growing number of satellites in orbit has resulted in a rise in defunct satellites and space debris, posing a significant risk to valuable spacecraft like normal satellites and space stations. Therefore, the removal of defunct satellites and space debris has become increasingly crucial. This article presents a segmentation method for satellite images captured in the visible light spectrum in space. Firstly, due to the lack of real space satellite images, we used optical simulation and Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for image-To-image Translation (U-GAT-IT) to generate realistic space satellite images in the visible light spectrum and constructed a dataset. Secondly, we proposed an Attention Supervision Transformer Full-Resolution Residual Network (ASTransFRRN), which integrates transformer, attention mechanism and deep supervision, to segment satellite bodies, solar panels, and the cosmic background. Finally, we evaluated the proposed method using the U-GAT-IT simulated dataset and compared its performance with state-of-The-Art methods. The proposed method achieved a segmentation accuracy of 90.77%±7.04% for satellite bodies, 90.61%±16.48% for satellite solar panels, and 97.66%±1.94% for the cosmic background. The overall pixel segmentation accuracy was 97.22%±2.78%, outperforming the compared methods in terms of segmentation accuracy. The proposed ASTransFRRN demonstrated a significant improvement in the segmentation accuracy of the main components of space satellites. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Space debris

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Anti-interference object detection algorithm based on spatio-Temporal behavior perception 12

Anti-interference object detection algorithm based on spatio...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Chen, Guo Han, Peng Mei, Jianwei Zuo, Zhengrong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In a military scenario, targets release decoy projectiles to achieve cover, then escape, evading the attack range of the guidance system. Therefore, recognizing target behaviors such as releasing decoys, escaping, and maneuvering, and locating the spatial position and behavior of the target can provide richer information for the precise targeting of the guidance system. Based on the behaviors of both the target and decoy, a behavioraware anti-interference algorithm is proposed. The core idea of this method is to link event recognition with spatio-Temporal action localization tasks. It extracts spatio-Temporal features of consecutive frames through a 3D backbone network and utilizes a 2D backbone network with a feature pyramid to extract spatial features at different levels of key frames for behavior detection at different scales. Additionally, a context extraction network is introduced in the key frames to achieve global context awareness, improving the accuracy of event recognition. Taking into account the correlation between spatial information, temporal information, and contextual information, this paper introduces channel attention mechanisms to fuse various feature components. Simultaneously, a non-local attention module is incorporated into the 3D backbone network to enhance the understanding of global content and the connection between different frames. Experimental results demonstrate that the proposed algorithm outperforms other algorithms, achieving a frame mean Average Precision (mAP) of 92.69% on our anti-interference dataset,enabling better resistance to interference. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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A semi-supervised network for object segmentation of remote sensing images 12

A semi-supervised network for object segmentation of remote ...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhang, Zhixin Liu, Jia Mou, Handong Liu, Tianhang Network & Information Center Changjiang Water Resources Commission Wuhan430010 China Center of Technology Innovation for Digital Enablement of River Basin Management Wuhan430010 China Smart Changjiang Innovation Team of Changjiang Water Resources Commission China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

Recently, object segmentation of remote sensing images has achieved great progress in many fields, such as transportation, natural resource, ecology, et al. A lot of works mainly performed object segmentation in fully-supervised mode. However, training models in such mode usually need craft large-scale annotations, which is usually an expensive work and costs much time. In this paper, a novel semi-supervised network for object segmentation of remote sensing images is proposed, which is only fed with a small amount of labeled data and relatively more unlabeled data. Rather than using the same architecture as previous semi-supervised works, we exploit two networks with different architectures, i.e. CNN and Transformer, as the cross-supervised models. Moreover, three types of loss functions, namely fully-supervised loss, cross-supervised loss and consistency loss, are introduced to enhance the model's robustness. The effectiveness of our proposed method is evaluated on two annotated remote sensing datasets, outperforming several state-of-The-Art semi-supervised approaches. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

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MIPPR 2023: Parallel processing of images and Optimization Techniques;And Medical Imaging

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The international Society for Optical Engineering 2024年 13087卷 IX-X页

作者： Liu, Jianguo Chen, Zhong Gao, Changxin Xiao, Yang Zhong, Sheng Hong, Hanyu Yue, Xiaofeng Huazhong Univ. of Science and Technology China Wuhan Institute of Technology China

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