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Deep learning-based cross-modal steel plate image detection and recognition 12

Deep learning-based cross-modal steel plate image detection ...

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

作者： Yang, Bingkun Wang, Hongwei Yue, Xiaofeng Dalian University of Technology dalian116081 China Huagong Tech Company Limited wuhan325204 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

The advancement and improvement of computer computing power have led to rapid development in the field of artificial intelligence. Intelligent information technology has also garnered attention and promotion in the manufacturing industry. However, existing research lacks consideration for the problems existing in the manufacturing field, mainly due to difficulties in acquiring rare scenario datasets. In the case of steel plate sorting, the detection of corner points plays a crucial role in production efficiency, particularly regarding the issue of steel plate adhesion caused by laser cutting. Considering the scarcity of seam-cut steel plate image data and the powerful generalization ability of cross-modal model GLIP, this study adopts the application approach of cross-modal large models from different fields. Firstly, we established a steel plate dataset with corner point information. And the GLIP model was fine-Tuned using weakly supervised learning. Then, the inference results of the large teacher model are used as inputs to the lightweight student model YOLOv8, forming a framework for lightweight deployment in the industry. In our experiments, we first compared the effects of different amounts of data on the GLIP model and then demonstrated that the 20-shot model performs comparably to the full-shot model. In addition, YOLOv8 can recognize corner points that have not been manually annotated or labeled by the GLIP model, demonstrating excellent generalization performance. We conducted comparative verification, which showed the advantages of GLIP in terms of time consumption, manually labeled data volume, and deployment scale. This study fully utilizes a sparsely labeled dataset and cross-modal large models, integrating them with a lightweight object detection model to reduce labeling costs and improve production efficiency. Finally, we propose directions for future work. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Sorting

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Research on the Impact of change features for Remote Sensing image Change Detection 12

Research on the Impact of change features for Remote Sensing...

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

作者： Mu, HaoDong Li, Haipeng Chen, Zhong Huazhong University of Science and Technology 1037 Luoyu Road HuBei WuHan430074 China Beijing Aerospace Automatic Control Institute Bejing100854 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

As remote sensing technology continues to advance, the accuracy and quantity of remote sensing images have significantly improved. The generation of a vast amount of available data has facilitated the widespread application of various deep learning methods in the field of remote sensing data processing, such as object detection, semantic segmentation, and change detection. In the aforementioned tasks, Change detection is used to identify alterations occurring on the Earth's surface by utilizing remote sensing (RS) data. In recent years, deep learning based methods have exhibited significantly superior performance compared to traditional change detection techniques. The fundamental strategy enabling these advancements involves extracting appropriate deep learning features from input remote sensing images through various backbone networks such as VGG, ResNet, DenseNet etc. Nevertheless, the features extracted by the aforementioned backbone networks may not fully cater to the specific requirements of remote sensing image change detection tasks. Consequently, our goal is to explore the influence of features extracted by different backbone networks on change detection tasks and introduce a specialized backbone network tailored for change detection. This endeavor aims to produce features that are better suited for the of change detection. The experimental results indicate that our specifically designed feature extraction network for remote sensing image change detection outperforms traditional networks in extracting task-specific features. These features are better suited for subsequent decoder modules, enhancing the generation of image-based change detection results. At the same time, we found that when using general backbone networks for change detection, ResNet achieves the highest metric accuracy, while DenseNet has the lowest memory usage and the fastest training and testing speed. Depending on the specific task, we can choose the appropriate backbone network as need

关键词： Feature extraction

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Infrared small target detection based on coarse-To-fine feature extraction network 12

Infrared small target detection based on coarse-To-fine feat...

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

作者： Han, Peng Zuo, Zhengrong National Key Lab of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

Infrared small target detection (IRSTD) is a technique that has been developed to detect small and faint targets in cluttered backgrounds. It shows extensive use in medical diagnosis and aircraft navigation. However, due to the limited feature information and low signal-To-noise ratio, accurately detecting infrared targets and avoiding false alarms is a challenging task. Our study presents a Coarse-To-Fine Feature Extraction Network (CFFENet) for accurately classifying targets and backgrounds in blurred images. This network consists of a Two-stream Region Proposal Network (2s-RPN) and a Region Refinement Attention Network (RRAN), specifically designed to tackle the problem of false alarms. Specifically, 2s-RPN adds edge features to the RPN, in order to enable the network to extract discriminative information between targets and false alarms, so as to provide reliable coarse candidate boxes. In RRAN, firstly, attention-based encoder is used to extract the attention-Aware features in candidate boxes, and predicted mask map is output through multi-layer perception (MLP). Subsequently, the refined feature map is further processed by attention encoder, and MLP is utilized to generate the ultimate detection results. The effectiveness of our approach is validated through comparative experiment, which shows its superiority over representative state-of-The-Art IRSTD methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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A shape and texture feature blending network for bone marrow cells classification using auxiliary learning 12

A shape and texture feature blending network for bone marrow...

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

作者： Guo, Zhengyang Yang, Zengbiao Xiang, Xuanyu 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)9781510674981

ISBN: (纸本)9781510674974

Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don't take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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Generative Adversarial Network-Based Jitter Distortion Correction for High Resolution Spaceborne images

Generative Adversarial Network-Based Jitter Distortion Corre...

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hao Wang Ying Zhu Lei Wang Lei Ma Jinmeng Wu Ting Li School of Electrical and Information Engineering Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China The Third Surveying and Mapping Institute of Hunan Province Changsha China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents a Generative Adversarial Network (GAN)-based jitter distortion correction method for spaceborne images of Time Delay Integration (TDI) Charge-Coupled Device (CCD) camera. This method leverages the advantages of GANs and combines content loss, adversarial loss, and perceptual loss to effectively repair distorted images while preserving image details, which does not rely on jitter information captured by high-frequency attitude sensors, nor depends on the analysis of overlapping areas between different bands in multispectral images. The experimental results show that the proposed method achieves automated correction of geometric distortions and has shown promising restoration results on real distorted images captured by Yaogan-26 satellite and GaoFen satellite, which achieves better results than other blind restoration methods.

关键词： Charge coupled devices Satellites Neural networks Manuals Jitter Maintenance engineering Generative adversarial networks

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Infrared spectrum image inversion method for augmenting data sets 11

Infrared spectrum image inversion method for augmenting data...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Liang, Shuai Liu, Meng Wang, Zhongyang Zhang, Tianxu Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781510636347

Based on the imaging mechanism of infrared image, we carry out the research of infrared spectral image inversion method for expanding data sets. Firstly, the radiation brightness of the long-wave infrared image is inverted to temperature, and then the radiation brightness of the medium-wave infrared image is calculated by using the inverted temperature. In order to improve the accuracy of infrared image inversion, the influence of range radiation and atmospheric transmittance on infrared radiation will be removed in the calculation process. Finally, we evaluate the effectiveness of this method.

关键词： Infrared image radiance temperature atmospheric transmittance

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Research on target salient region unmixing method 11

Research on target salient region unmixing method

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11th international symposium on multispectral image processing and pattern recognition: multispectral image Acquisition, processing, and Analysis, MIPPR 2019

作者： Lv, Siman Zhang, Tianxu Dong, Shuai Wuhan Institute of Technology Wuhan430205 China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510636330

Because the structure of the moving target in the air is complex and its composition is not single, it is impossible to simply regard the measured spectral signal as the sum of all parts. For spectral acquisition of complex small target in salient region, a target saliency region unmixing method is innovatively proposed in this paper. The abundance information of sky background, target region and salient regions was obtained by analyzing the infrared image. According to the measured spectra and their weighted linear combination relationship, the precise spectral signal of the target saliency region can be estimated. The experimental results show that, through this method, the spectra of each salient regions on the target is more accurate and have more abundant details. The comprehensive judgment result of each salient regions spectra can effectively improve the target recognition accuracy. © 2020 SPIE.

关键词： Iterative methods

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A new aircraft classification algorithm based on sum pooling feature with remote sensing image 11

A new aircraft classification algorithm based on sum pooling...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Li, Binzhe Hu, Jing Fang, Li Kang, SuSu Li, XiangJun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Natl Key Sci & Technol Multispectral Informat Pro Wuhan 430074 Peoples R China Yanan Univ Sch Phys & Elect Yanan 716000 Peoples R China

ISBN: (纸本)9781510636385

For the problem that VGG network cannot use the special information of feature maps, this paper proposes a new algorithm that constructs the sum pooling feature based on the feature map extracted by the convolutional neural network. And this algorithm retains the construction of original feature maps so that special information on the original feature map could be used more reasonably. And then, this paper uses DOTA datasets to verify the proposed method. The results show that compared with the VGG-16 network, the proposed SPFC algorithm improves the accuracy in rough aircraft classification and the fighter subdivision.

关键词： aircraft classification sum pooling feature VGG network remote sensing image

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A NEW TEMPORAL HIGH-PASS ADAPTIVE FILTER NONUNIFORMITY CORRECTION BASED ON ROLLING GUIDANCE FILTER 11

A NEW TEMPORAL HIGH-PASS ADAPTIVE FILTER NONUNIFORMITY CORRE...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Yuan, Yuan Song, Qiong Guo, Xin Wang, Yuehuan Sci & Technol Low Light Level Night Vis Lab Kunming Yunnan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781510636347

In this paper, we introduce a novel nonuniformity correction (NUC) algorithm for infrared focal-plane array (IRFPA). It is based on layers technique. First, the Rolling Guidance Filter (RGF) is utilized to decompose the raw IR image into a low frequency part and a high frequency part. Then, an adaptive temporal high-pass filter is utilized to filter the high frequency part by making use of the gradient and amplitude of it to estimate the Fixed pattern Noise (FPN). The proposed scheme use the frames with large displacement to estimate the FPN to alleviate the ghosting artifacts in case of scene moves slowly. At Last, the estimated FPN is subtracted from the pristine image to obtain the correct result. Experiments with synthetic and real IR video demonstrate that the proposed method has better NUC performance and less artifacts than the state-of-the-arts.

关键词： Fixed pattern noise Nonuniformity correction Infrared imaging Temporal high pass

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Research on vehicle detection method for multi-polarimetric SAR image based on scene analysis 11

Research on vehicle detection method for multi-polarimetric ...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Zhang, Bochuan Shao, Xuehui Nie, Peng Hu, Ruiguang Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligent Con Beijing 100854 Peoples R China

ISBN: (纸本)9781510636385

Aiming at the vehicle target detection problem of multi-polarization SAR image under the terrain backgrounds, the global CFAR detection by dual-polarized 16-bit data is proposed, which effectively reduces the influence of terrain clutter on detection. In addition, through the analysis of terrain flatness and the screening of built-up areas, the target detection area is reduced, and the interference of the complex terrain and the densely populated area to the target detection is further reduced, and the reliability of the target detection is greatly improved.

关键词： scene matching analysis of background characteristic multi-polarimetric image

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