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Systematic literature review of AI algorithms applied to unmanned aerial vehicle images

INTERNATIONAL JOURNAL OF image AND DATA FUSION 2025年第1期16卷

作者： El Khadir, Kenza Ait Fadil, Abdelhamid El Brirchi, El Hassan Hassania Sch Publ Works LaGes Lab Km 7 Jadida RdBP 8108 Casablanca Morocco

Artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAVs technologies.

关键词： Artificial intelligence ML image processing computer vision UAVs (unmanned aerial vehicles) drones image recognition fault detection object detection inspection agriculture environmental pollution CNNs (convolutional neural networks) infrastructure

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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5th International Conference on Data Science, machine Learning and applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer vision Natural Language processing Attention Mechanism Performance

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A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年 1-33页

作者： Pan, Xiao Yang, Tony T. Y. Li, Jun Ventura, Carlos Malaga-Chuquitaype, Christian Li, Chaobin Su, Ray Kai Leung Brzev, Svetlana Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Clear Water Bay Hong Kong Peoples R China Univ British Columbia Dept Civil Engn Vancouver BC Canada Curtin Univ Ctr Infrastruct Monitoring & Protect Perth WA Australia Imperial Coll London Dept Civil & Environm Engn London England Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China

Computer vision techniques have gained great traction in civil infrastructure inspection and monitoring. This paper conducted a systematic review of recent data-driven computer vision algorithms in structural damage detection published during the past 5 years. The theories of prevalent computer vision models are first reviewed with an emphasis on the progressive innovation in algorithms' architecture. Then, recent applications of computer vision models for structural damage evaluation are discussed, which are classified into different structural categories by their material types (i.e., concrete, steel, masonry, timber) at three hierarchical levels including damage recognition, localization, and quantification. In particular, the paper also highlights the current state of using computer vision for damage assessment of timber structures, which remains under-explored compared to concrete and steel structures. Next, the paper scrutinizes existing structural damage inspection guidelines to identify key technological gaps between the capability of existing computer vision methods and manual inspection practices in the field. Finally, the paper summarizes existing challenges and recommends future research opportunities including the integration of computer vision methods with multimodal large language models, sensor-fusion, and mobile inspection approaches.

关键词： Structural damage detection Structural health monitoring Computer vision image processing machine learning Deep learning Sensor fusion Unmanned aerial and ground vehicles

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Ipdm: identity preserving diffusion model for face sketch and photo synthesis

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machine vision AND applications 2025年第2期36卷 1-14页

作者： Tang, Duoxun Jiang, Xinhang Zhang, Ying Dai, Yuhang Lin, Ye Sichuan Agr Univ Coll Sci Yaan 625014 Sichuan Peoples R China Sichuan Agr Univ Coll Informat Engn Yaan 625014 Sichuan Peoples R China

Face sketch and photo synthesis is widely applied in industry and information fields, such as entertainment business and heterogeneous face retrieval. The key challenge lies in completing a face transformation with both good visual effects and face identity preservation. However, existing methods are still difficult to obtain a good synthesis due to the large model gap between the two different face domains. Recently, diffusion models have achieved great success in image synthesis, which allows us to extend its application in such a face generation task. Thus, we propose IPDM, which constructs a mapping of latent representation for domain-adaptive face features. The other proposed IDP utilizes auxiliary features to correct the latent features through their directions and supplementary identity information, so that the generation can keep face identity unchanged. The various evaluation results show that our method is superior to state-of-the-art methods in both identity preservation and visual effects.

关键词： Face sketch synthesis Identity preservation Diffusion model Generative model image processing

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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th International Conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： machine Learning Deep Learning Computer vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2025年第1期25卷 1-17页

作者： Kallipolitis, Athanasios Moutselos, Konstantinos Zafeiriou, Argyrios Andreadis, Stelios Matonaki, Anastasia Stavropoulos, Thanos G. Maglogiannis, Ilias Univ Piraeus Dept Digital Syst Piraeus Greece Pfizer Ctr Digital Innovat Thessaloniki Greece

Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review examines deep learning architectures and image processing algorithms for segmentation, feature extraction, and classification tasks employed for disease detection. It also focuses on practical applications, emphasizing quantitative disease assessment, and the performance of various computer vision approaches for each condition while highlighting their strengths and limitations. Finally, the review denotes the need for disease-specific datasets with curated annotations and suggests future directions toward unsupervised or self-supervised approaches. Additionally, the findings underscore the importance of developing accurate, automated tools for disease severity score calculation to improve ML-based monitoring and diagnosis in dermatology.

关键词： Skin image analysis Benign skin lesions Dermatitis Alopecia Areata Vitiligo machine learning

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Ultrabroadband Detection and Self-Powered Functionality in Quasi-One-Dimensional Nb3Se12I Nanowire Photodetectors for Bionic vision applications

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ACS APPLIED MATERIALS & INTERFACES 2025年第14期17卷 21448-21458页

作者： Zhang, Jianbin Zhao, Yi Liu, Ge Wang, Guangyi Chen, Liangqiang Shang, Conghui Li, Jiaxuan Zhou, Nan Xu, Hua Yang, Rusen Li, Xiaobo Xidian Univ Sch Adv Mat & Nanotechnol Shaanxi Joint Key Lab Graphene Shaanxi Key Lab High Orbits Electron Mat & Protect Xian 710126 Peoples R China Shaanxi Normal Univ Shaanxi Engn Lab Adv Energy Technol Key Lab Appl Surface & Colloid Chem Shaanxi Key Lab Adv Energy DevicesMinist EducSch Xian 710119 Peoples R China

The burgeoning fields of the Internet of things (IoT) and artificial intelligence (AI) have escalated the demands for image sensing technologies, necessitating advancements in sensor efficiency and functionality. Traditional image sensors, structured on von Neumann architectures with discrete processing units, face challenges, such as high power consumption, latency, and escalated hardware costs. In this work, we introduced a unique approach through the development of a quasi-one-dimensional nanowire Nb3Se12I-based double-ended photosensor. The advanced sensor not only replicated the adaptive behavior of biological vision systems but also effectively managed the decreased sensitivity triggered by intense light stimuli. The integration of the photothermoelectric and bolometric effects allows the device to operate in a self-powered mode, offering broadband detectivity ranging from visible (405 nm) to midwave infrared (4060 nm). Additionally, the quasi-one-dimensional structure enables an angle-dependent response to polarized light with a polarization ratio of 1.83. Our findings suggest that the biomimetic vision adaptive sensor based on Nb3Se12I could effectively enhance the capabilities of smart optical sensors and machine vision systems.

关键词： photothermoelectric self-powered Nb3Se12I ultrabroadband detection bionicvisual adaptation

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TGGLinesPlus: A Robust Topological Graph-Guided Computer vision Algorithm for Line Detection From images

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TRANSACTIONS IN GIS 2025年第1期29卷

作者： Yang, Liping Driscol, Joshua Gong, Ming Slack, Katie Zhang, Wenbin Wang, Shujie Potts, Catherine G. Univ New Mexico Dept Geog & Environm Studies GeoAIR Lab Albuquerque NM 87106 USA Univ New Mexico Ctr Adv Spatial Informat Res & Educ ASPIRE Albuquerque NM 87106 USA Univ New Mexico Dept Comp Sci Albuquerque NM 87106 USA Woodwell Climate Res Ctr Falmouth MA USA Univ Dayton Dept Elect & Comp Engn Dayton OH USA Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL USA Penn State Univ Dept Geog University Pk PA USA Penn State Univ Earth & Environm Syst Inst University Pk PA USA D Wave Quantum Inc Burnaby BC Canada

Line detection is a classic and essential problem in image processing, computer vision, and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus.

关键词： algorithms computational geometry computer vision edge and feature detection graph theory image processing line detection spatial sciences topological graph

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Annotation Tools for Computer vision Tasks 17

Annotation Tools for Computer Vision Tasks

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17th International Conference on machine vision, ICMV 2024

作者： Moschidis, Christos Vrochidou, Eleni Papakostas, George A. Department of Informatics Democritus University of Thrace Kavala65404 Greece

ISBN: (纸本)9781510688278

Common computer vision (CV) tasks include image classification, object detection, segmentation, and recognition. To handle such tasks, machine learning (ML) models for image processing require a great amount of annotated training data. While datasets are expanding in size and variety, annotation becomes demanding, since its quality can severely affect the models' performance. Thus, several annotation tools have been developed and designated for specific applications and model requirements. This work aims to provide an overview of the most up-to-date annotation tools for computer vision tasks, including 2D and 3D image data and video, comparatively highlighting their advantages and limitations. The appropriateness of each tool for specific tasks is emphasized, providing a reference map for researchers towards determining the annotation tool best tailored to their needs. Future trends in image annotation are also discussed. © 2025 SPIE.

关键词： image annotation

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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and machine

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IEEE ACCESS 2025年 13卷 43289-43315页

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Inst Telecomunicacoes P-1049001 Lisbon Portugal Politecn Leiria ESTG P-2411901 Leiria Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.

关键词： Transform coding Point cloud compression Encoding Standards image coding Three-dimensional displays Geometry image color analysis Artificial intelligence Codecs JPEG Pleno standard learning-based coding man and machine point cloud coding

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