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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P Xie, Zhiying Yang, Aihua He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment Dongting Lake Area Changsha Hunan410004 China

ISBN: (纸本)9798400716942

In the protection and restoration work of ancient buildings, the detection and analysis of the cracks and defects of the exterior walls of ancient buildings is a crucial task. In this paper, an improved YOLOv7 algorithm is proposed to detect the defects of ancient external walls, aiming to achieve efficient and accurate detection of the defects of ancient external walls. The method involves preprocessing the image using a rectangle clipping algorithm, graying the image using the maximum method, and denoising the image with a median filter. Next, the image is labeled using the LabelImg tool, and the data is split into training, validation, and test sets. During model training, a CBAM-Net module is inserted after several CBS modules in the backbone network, the GIOU loss function is replaced by CIOU, and feature task extraction is performed. The realized attention mechanism is added to the Yolov7 model. The results show that the mAP value of crack detection after the improved algorithm reaches 81.2%. In contrast to the initial YOLOv7 algorithm, the precision of the model has been boosted to 87.2%, exhibiting a 6.0% enhancement in accuracy and an 85.2% improvement in recall rate. The improved YOLOv7 crack detection algorithm reduces network calculation and improves crack classification and recognition ability. This approach is crucial in safeguarding ancient buildings as it enables varying levels of maintenance depending on the severity of cracks. © 2024 ACM.

关键词： Attention mechanism Deep learning Defect detection image processing YOLOv7

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Semantic recognition and detection method based on DSM multi-scale image segmentation technology 3

Semantic recognition and detection method based on DSM multi...

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3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Jiang, Shuo Ji, Xiangtian SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781510671423

This project will study a digital surface elevation model (DSM) and an image semantic segmentation method based on multi-scale weight clustering. Taking the overall partitioning metric as a constraint, the weights are classified step by step using a multi-grid algebraic algorithm. An image processing method based on a single residual method is proposed for high-resolution remote sensing images. Row Height Information Mining with Convolutional Neural Networks is proposed. The method's accuracy can reach 92.33% through the test on the existing public database. The experimental results prove that the method extracts and fuses features of multiple levels and can obtain better semantic segmentation results. © 2023 SPIE.

关键词： Semantic Segmentation

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Dynamic Task-Oriented Prompting for Generalized Aerial Category Discovery

Dynamic Task-Oriented Prompting for Generalized Aerial Categ...

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image processing Challenges (ICIPC), IEEE International Conference on

作者： Muli Yang Huaiyuan Qin Hongyuan Zhu Institute for Infocomm Research (I2R) A*STAR Singapore

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

remote sensing plays a crucial role in monitoring Earth’s surface and atmosphere, supplying valuable data on environmental factors such as land cover and vegetation health, which are essential for decision-making in agriculture, urban planning, disaster management, and other fields. With the advent of AI, researchers are striving to automate remote sensing image analysis by leveraging large pre-trained foundation models. However, adapting these models to remote sensing imagery poses significant challenges, particularly in fine-tuning them with sufficient labeled data, which can be costly and impractical. Moreover, existing methods may struggle to identify unknown categories in rapidly evolving environments. To tackle these issues, we have developed Dynamic Task-oriented Prompting (DTP), a technique that enables foundation models to effectively recognize both known and unknown scene categories in remote sensing imagery using limited labeled data. Extensive experiments have shown its promise in adapting foundation models to remote sensing imagery within dynamic environments.

关键词： image recognition image analysis Urban planning Terrestrial atmosphere Land surface Vegetation mapping Environmental factors Sensors remote sensing Monitoring

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Target recognition System of Football remote sensing image Based on Heapsort Algorithm

Target Recognition System of Football Remote Sensing Image B...

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Electronics, Data processing and Informatics (ACEDPI), Asia-Europe Conference on

作者： Ming Zhang Ding Peng Jiangxi University of Software Professional Technology Nanchang Jiangxi China

Since its birth, football has become a world-class sport with countless fans. With the continuous development of football, fans are increasingly looking forward to seeing more exciting football games. The article is based on the Heapsort algorithm-based football remote sensing image target recognition system. On the basis of the literature, the Heapsort algorithm and the relevant theories of the football remote sensing image target recognition are understood, and then the football remote sensing image target recognition based on the Heapsort algorithm the system is designed, and the accuracy of the detection system is improved by introducing the Heapsort algorithm into the target detection process, and then the designed system is detected. The detection result shows that the tracking accuracy of the system in this paper is about 90%.

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Clothing image Retrieval Algorithm Using YOLOv8 and Attention Model

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pattern recognition AND image ANALYSIS 2024年第4期34卷 1105-1110页

作者： Shiping, Ye Sorokina, V. Ablameyko, S. Zhejiang Shuren Univ Hangzhou 310015 Peoples R China Int Sci & Technol Cooperat Base Zhejiang Prov Remote Sensing Image Proc & Applicat Hangzhou 310000 Peoples R China Belarusian State Univ Minsk 220050 BELARUS Natl Acad Sci Belarus United Inst Informat Problems Minsk 220012 BELARUS

Nowadays image clothing retrieval is one of the key tasks in the e-commerce sphere. In recent years, several different approaches have emerged to help solve this problem. In this paper, the algorithm for image clothing retrieval in the e-commerce sphere using state-of-the-art computer vision model and attention model is presented. The innovation of the proposed algorithm is its modular architecture, which allows for more efficient extraction of image features and their further analysis using an attention model. One more advantage of the algorithm is the possibility to use its modules independently so that not the only image retrieval task could be solved. The algorithm is validated in the real e-commerce application and compared with existing e-commerce retrieval algorithms proving to successfully solve the task. Experiments showed that the developed algorithm achieved state-of-the-art results, reaching an accuracy of 94% for top-3 and 96% for top-5 image retrieval, with real-time speed on a graphics processing unit.

关键词： attention model convolutional neural network image retrieval segmentation e-commerce YOLOv8

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Landslide recognition by Deep Convolutional Neural Network and Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2021年第6期59卷 4654-4672页

作者： Shi, Wenzhong Zhang, Min Ke, Hongfei Fang, Xin Zhan, Zhao Chen, Shanxiong Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China

It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km(2). Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability.

关键词： Terrain factors Feature extraction Training Data mining image recognition Convolutional neural networks remote sensing Change detection convolutional neural network (CNN) landslide remotely sensed (RS) images

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JOINT SUPER-RESOLUTION AND image RESTORATION FOR PLEIADES NEO imageRY 24

JOINT SUPER-RESOLUTION AND IMAGE RESTORATION FOR PLEIADES NE...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Chouteau, F. Gabet, L. Fraisse, R. Bonfort, T. Harnoufi, B. Greiner, V Le Goff, M. Ortner, M. Paveau, V Airbus Def & Space Upstream Ground Syst 31 Rue Cosmonautes F-31400 Toulouse France Airbus Def & Space Geo Intelligence 15 Rue Satellites F-31030 Toulouse France

Modern Earth Observation optical satellite systems, such as Airbus's Pleiades Neo (PNeo) push the boundaries of high spatial resolution by providing commercial imagery products with up to 30cm ground sampling distance (GSD). To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012). Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K. Zhang et al., 2021a). Single image Super-Resolution (SISR), or Zoom, the process of obtaining a higher resolution (HR) image from a lower resolved (LR) source, has recently gained traction for both medium resolution sensors such as Sentinel 2 (Lanaras et al., 2018) and high resolution such as Pleiades and GeoEye-1 (Zhu et al., 2020). This process further enhances the resolution of the image to improve downstream applications such as mapping (L. Zhang et al., 2021) and small objects recognition (Shermeyer and Van Etten, 2019). While SISR for remote sensing has been successfully tackled using CNNs (Rohith and Kumar, 2021) the main challenge for reaching acceptable image quality performance lies in the generation of realistic LR/HR training pairs (K. Zhang et al., 2021b). In this paper, we propose: a dedicated simulation chain leveraging extremely-high-resolution (EHR) aerial imagery to generate realistic 30cm Pleiades Neo images and their corresponding fully restored HR equivalent at 15cm GSD A residual-based CNN architecture which we train to jointly restore and zoom the images All contributions are assessed on real PNEO images. We deployed the trained models in a production context, to enhance the full Pleiades Neo products - with a swath of 47k pixels - in an efficient and scalable manner.

关键词： Single-image Super-Resolution Pleiades Neo remote sensing Deep Learning Convolutional Neural Networks

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Small Object Detection in Medium-Low-Resolution remote sensing images Based on Degradation Reconstruction

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remote sensing 2024年第14期16卷 2645-2645页

作者： Zhao, Yongxian Sun, Haijiang Wang, Shuai Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

With the continuous development of space remote sensing technology, the spatial resolution of visible remote sensing images has been continuously improved, which has promoted the progress of remote sensing target detection. However, due to the limitation of sensor lattice size, it is still challenging to obtain a large range of high-resolution (HR) remote sensing images in practical applications, which makes it difficult to carry out target monitoring in a large range of areas. At present, many object detection methods focus on the detection and positioning technology of HR remote sensing images, but there are relatively few studies on object detection methods using medium- and low-resolution (M-LR) remote sensing images. Because of its wide coverage area and short observation period, M-LR remote sensing imagery is of great significance for obtaining information quickly in space applications. However, the small amount of fine-texture information on objects in M-LR images brings great challenges to detection and recognition tasks. Therefore, we propose a small target detection method based on degradation reconstruction, named DRADNet. Different from the previous methods that use super resolution as a pre-processing step and then directly input the image into the detector, we have designed an additional degenerate reconstruction-assisted framework to effectively improve the detector's performance in detection tasks with M-LR remote sensing images. In addition, we introduce a hybrid parallel-attention feature fusion module in the detector to achieve focused attention on target features and suppress redundant complex backgrounds, thus improving the accuracy of the model in small target localization. The experimental results are based on the widely used VEDAI dataset and Airbus-Ships dataset, and verify the effectiveness of our method in the detection of small- and medium-sized targets in M-LR remote sensing images.

关键词： remote sensing deep learning medium-low-resolution images object detection super resolution

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Using Direct-Georeferencing with a MEMS-LiDAR to Classify Traffic Signs, an Automatic Approach 5

Using Direct-Georeferencing with a MEMS-LiDAR to Classify Tr...

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5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Liu, Xuanzhi Khoramshahi, Ehsan Zhang, Ruochen Chen, Chen Puttonen, Eetu Chen, Yuwei Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute Espoo Finland Photogrammetry and remote-sensing section Visionium Oy Helsinki Finland School of Engineering Science Lappeenranta University of Technology Lahti Finland

ISBN: (纸本)9798350354294

In this article, a micro-electromechanical systems light-detection and ranging (MEMS-LiDAR) based sampling method for traffic sign recognition is presented. MEMS-LiDAR is an emerging remote-sensing technology in the autopilot (AP) area. Existing published scientific reports have been less concerned with using point-cloud datasets sampled from MEMS-LiDAR, as the MEMS-LiDAR is a recently matured commercial technology. In this paper, we propose utilizing a MEMS-LiDAR system (Robosense-M1) for data acquisition in a mobile laser scanning (MLS) in an urban environment. Subsequently, two methods related to clustering and point-cloud 3D geometric feature extraction are introduced to segment and filter out plain-like objects. Finally, a support vector machine method (SVM) is investigated for the task of traffic-sign classification. Our results successfully demonstrate the effectiveness of the proposed method with K1-score based on the MEMS-LiDAR sampled data to be 83.33%. This result shows the effectiveness of the proposed method. © 2024 IEEE.

关键词： Support vector machines

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A Lightweight Serial CNN Model for remote sensing Ship Target recognition on FPGA

A Lightweight Serial CNN Model for Remote Sensing Ship Targe...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Wang, Changyi Yao, Bowen Liu, Liansheng Peng, Yu Harbin Institute of Technology School of Electronics and Information Engineering Harbin150080 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Ship target recognition with remote sensing is an important task for earth observation. The excessive recognition delay may render the results meaningless for time-sensitive targets such as ships. Therefore, rapid and timely analysis of image data on the space-borne platform is required. With the development of deep learning, Convolution Neural Network (CNN)-based methods have achieved high accuracy in the ship recognition task. The huge model size and computational complexity make the CNN-based methods difficult to be deployed on the space-borne platform with limited resources. Hence, model compression techniques are adopted in some on-board methods to obtain the real-time information of ships. However, these methods can only locate ships and cannot give specific category information. To address this problem, a lightweight ship recognition method based on the subgraph cascade classification is proposed. Firstly, the entire image is divided into small subgraphs. Secondly, a lightweight CNN is adopted to select ships from backgrounds. Then, the accurate coordinates of ships are obtained through position regression. At last, ship targets are classified as civil ships and other ships. The experiments show that the method has a promising application prospect in on-board intelligent processing. © 2022 IEEE.

关键词： Optical remote sensing

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