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RT-Droid: a novel approach for real-time android application analysis with transfer learning-based CNN models

JOURNAL OF real-time image processing 2023年第3期20卷 1-17页

作者： Tasyurek, Murat Arslan, Recep Sinan Kayseri Univ Dept Comp Engn Kayseri Turkiye

Today, the number, type and complexity of malware is increasing rapidly. Convolution neural network (CNN) based networks continue to be used in software classification based on image. In this study, a CNN model named real time-Droid(RT-Droid), which has a very fast malware detection capability and works based on YOLO V5, is introduced. RT-Droid detects android malware with high accuracy and performs this process at near real-time speed. For this process, firstly the features in the android manifest file are extracted and converted to an image in RGB format similar to QR code. Thus, images become processed by CNN-based deep learning models. These images were used to train VGGNet, Faster R-CNN, YOLO V4 and V5 models with the transfer learning technique. The android malware detection performances of the obtained trained models (weights) were examined. In the tests performed with Drebin, Genome and Arslan dataset, the precision value is 98.3%, while the F-score value is 97.0%. In obtaining these values, only 0.019 s per application was needed for analysis. It also requires 25 times less memory space compared to a gray-scale image. Since the small images of the YOLO V5 model can detect objects with very high accuracy and in real time, it provides serious efficiency in processing time. We also compared the results with VGGNet, Faster R-CNN and YOLO V4, which are commonly used CNN models for object detection, and show that it yields results at a higher rate and at least 5.5 times faster than similarly trained networks. Our method detects hacker-generated Android malware very quickly and with high accuracy, while being robust against obfuscated apps.

关键词： image based malware detection Convolutional neural network Transfer learned Android malware Cybersecurity QR-code

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Two-stage deep learning-based video image recognition of early fires in heritage buildings

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Gao, Shenyuan Huang, Guozhong Chen, Xiaolong Jiang, Huiling Liang, Zhou Gao, Xuehong Univ Sci & Technol Beijing Res Inst Macrosafety Sci Beijing Peoples R China Huawei Technol CO LTD Guangzhou Peoples R China

Fire poses a huge safety risk to heritage or historic buildings. Traditional approaches are unsuitable because of their high false alarm rates, delayed features, and vulnerability in heritage building environments. To reduce the serious consequences caused by fires, it is necessary to develop advanced approaches to achieve real-time early fire detection in heritage buildings. Hence, this study proposed a two-stage deep learning-based video image recognition of real-time fires in heritage buildings. Motion detection based on a Gaussian mixture model was applied to determine moving objects in the first stage. Then, a Fire-Det model for the indoor environment and a Fire-Det Nano model for outdoor areas were developed in the second stage. To validate the proposed models, a dataset including negative images was built and tested. To illustrate the performance of this study, the proposed approaches were compared with traditional methods. It is found that the Fire-Det model can complete fire detection at the fastest reasoning speed without losing generalizability of accuracy. The proposed Fire-Det Nano model can also drastically reduce computation time at the expense of a slight reduction in accuracy. Fire-Det is the innovation in AI. Fire-Det Nano and motion detection are applications in engineering. These findings indicate that the proposed method has great potential significance in the application of early-fire detection in heritage buildings, such as the Forbidden City.

关键词： Fire detection Heritage buildings deep learning Motion detection

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SDHS-RLDNet: A real-time lightweight detection network for small-dense photovoltaic hot-spots

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JOURNAL OF real-time image processing 2025年第1期22卷 1-13页

作者： Hao, Shuai Li, Tong Ma, Xu Li, Tianqi Chang, Chi Qi, Tianrui Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China

To ensure the safe and stable operation of photovoltaic power plants, it is crucial to conduct regular fault inspections on solar arrays. In a complex inspection environment, it is difficult to ensure the accuracy of detecting small and densely distributed photovoltaic hot-spot faults using traditional algorithms, and real-time detection is also challenging. To solve the above issues, a real-time lightweight detection network for small and dense photovoltaic hot-spots is proposed. Firstly, a multi-scale target extraction module is designed to enhance the feature extraction capability of the backbone network, which can effectively detect faults at different scales. Additionally, a small target prediction head is added to improve the detection performance of small hot-spot faults. Secondly, a dense object detection module is designed to enhance the positional information of hot-spot faults and effectively suppress background interference caused by complex backgrounds. Furthermore, to achieve network lightweighting, the method of knowledge distillation is adopted. By transferring the parameters of the teacher network to the student network, it simplifies the network parameters, improves model inference speed, and ensures real-time detection performance of the network. Finally, to verify the superiority of the proposed network, seven classical algorithms are selected for comparison experiments. The experimental results demonstrate that SDHS-RLDNet can accurately detect multi-scale hot-spot faults under various conditions with an accuracy rate reaching 86.6%.

关键词： deep learning real-time detection Lightweight network Fault inspection Hot-spot detection Knowledge distillation

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real-time Batch Detection System for Defects in Highly Curved Spiral Cylindrical Coils

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IEEE ACCESS 2024年 12卷 153370-153384页

作者： Zhuo, Qinghang Kong, Linghua Zheng, Jishi Ding, Zhigang Li, Wenguang Yi, Dingrong Lian, Guofu Fujian Univ Technol Sch Mech & Automot Engn Fuzhou 350118 Peoples R China Fujian Univ Technol Digital Fujian Ind Mfg Internet Things Lab Fuzhou 350118 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China Huaqiao Univ Sch Mech Engn & Automat Xiamen 361021 Peoples R China

Tungsten coil is one of the important components of the magnetron, and its surface quality and geometry directly affect the service life of the magnetron. The focus of this article is to apply machine vision and deep learning algorithms to study a surface defect detection and geometric measurement system for tungsten coils. Firstly, in order to solve the problem of difficulty, high cost, and low efficiency in manual detection caused by small and random surface cracks on tungsten coils, this paper proposes a surface crack detection method based on deep learning technology. By constructing the dataset and training the semantic segmentation model, the surface cracks were effectively detected and identified. Secondly, a sub-pixel edge detection method based on image processing technology and the Canny algorithm is proposed to address the issue of difficulty in measuring the geometric dimensions of tungsten coils due to their high bending spiral structure. The method accurately measures the length, outer diameter, and the maximum error is only 0.023mm. The results of the verification experiment indicate that the developed system can detect an average of 70 tungsten coil samples in one minute, with sensitivity and accuracy of 99.66% and 98.52%, respectively. This system not only has high robustness and efficiency, but also reduces 95.23% of manual workload, meeting the requirements of production lines for surface defect detection and geometric dimension measurement of tungsten coils, and solving the limitations of traditional manual detection.

关键词： Coils Tungsten Surface cracks Accuracy Defect detection Magnetic field measurement Inspection Machine vision image processing Tungsten coil machine vision defect detection dimensional measurement image processing

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Non-invasive vision-based personal comfort model using thermographic images and deep learning

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AUTOMATION IN CONSTRUCTION 2024年 168卷

作者： Zakka, Vincent Gbouna Lee, Minhyun Zhang, Ruixiaoxiao Huang, Lijie Jung, Seunghoon Hong, Taehoon Aston Univ Coll Engn & Phys Sci Birmingham England Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Yonsei Univ Dept Architecture & Architectural Engn Seoul South Korea

An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive visionbased personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.

关键词： Occupant thermal comfort Personal comfort model Thermographic imaging deep learning Non-invasive approach Occupant-centric control

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Enhancing trabecular CT scans based on deep learning with multi-strategy fusion

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2024年 116卷 102410-102410页

作者： Ge, Peixuan Li, Shibo Liang, Yefeng Zhang, Shuwei Zhang, Lihai Hu, Ying Yao, Liang Wong, Pak Kin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Shenzhen Polytech Univ Sch Automot & Transportat Engn Shenzhen 518055 Guangdong Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Orthopaed Beijing Peoples R China Univ Macau Dept Electromech Engn Macau 999078 Peoples R China

Trabecular bone analysis plays a crucial role in understanding bone health and disease, with applications like osteoporosis diagnosis. This paper presents a comprehensive study on 3D trabecular computed tomography (CT) image restoration, addressing significant challenges in this domain. The research introduces a backbone model, Cascade-SwinUNETR, for single-view 3D CT image restoration. This model leverages deep layer aggregation with supervision and capabilities of Swin-Transformer to excel in feature extraction. Additionally, this study also brings DVSR3D, a dual-view restoration model, achieving good performance through deep feature fusion with attention mechanisms and Autoencoders. Furthermore, an Unsupervised Domain Adaptation (UDA) method is introduced, allowing models to adapt to input data distributions without additional labels, holding significant potential for real-world medical applications, and eliminating the need for invasive data collection procedures. The study also includes the curation of a new dual-view dataset for CT image restoration, addressing the scarcity of real human bone data in Micro-CT. Finally, the dual-view approach is validated through downstream medical bone microstructure measurements. Our contributions open several paths for trabecular bone analysis, promising improved clinical outcomes in bone health assessment and diagnosis.

关键词： Trabecular CT analysis Medical image restoration Dual-view learning Unsupervised domain adaptation deep supervision Medical image segmentation Medical image processing

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Road Intersection Analysis: Integrating image processing into Digital Twin Technologies

Road Intersection Analysis: Integrating Image Processing int...

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Conference on real-time processing of image, Depth, and Video Information

作者： Vdzquez Donaire, Francisco C. Abalo-Garcia, Alejandra Montemayor, Antonio S. Pantrigo, Juan J. Univ Rey Juan Carlos Calle Tulipan S-N Mostoles Spain

ISBN: (纸本)9781510673199;9781510673182

We present a real-time system for vehicle detection and classification in road intersections, incorporating image processing techniques. This system estimates the traffic flow at a specific point, as it is capable of recognizing the trajectories of different vehicles at an intersection, inferring whether they leave or enter the city. It is designed to be integrated into a high-fidelity digital twin, aiding in estimating environmental traffic pollutants. Since Computational Fluid Dynamics (CFD) use estimators like average or aggregate measurements, we use more accurate methods to estimate pollution. The implications of our study are significant for urban planning and traffic management. It allows for immediate decisions and informs long-term infrastructure planning by providing a deep understanding of intersection dynamics. Our research offers a comprehensive perspective on traffic analysis, introducing data-driven traffic management strategies for efficient urban mobility. The code developed for this purpose can be found in https://***/capo- urjc/TrackingSORT

关键词： deep learning digital twins traffic monitoring visual tracking

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Optimized Autonomous Drone Navigation Using Double deep Q-learning for Enhanced real-time 3D image Capture

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DRONES 2024年第12期8卷

作者： Sanchez-Soriano, Javier Rojo-Gala, Miguel angel Perez-Perez, Guillermo Bemposta Rosende, Sergio Gordo-Herrera, Natalia Univ Francisco Vitoria Escuela Politecn Super Pozuelo De Alarcon 28223 Spain Univ Europea Madrid Dept Sci Comp & Technol Villaviciosa De Odon 28670 Spain

The proposed system assists in the automatic creation of three-dimensional (3D) meshes for all types of objects, buildings, or scenarios, using drones with monocular RGB cameras. All these targets are large and located outdoors, which makes the use of drones for their capture possible. There are photogrammetry tools on the market for the creation of 2D and 3D models using drones, but this process is not fully automated, in contrast to the system proposed in this work, and it is performed manually with a previously defined flight plan and after manual processing of the captured images. The proposed system works as follows: after the region to be modeled is indicated, it starts the image capture process. This process takes place automatically, with the device always deciding the optimal route and the framing to be followed to capture all the angles and details. To achieve this, it is trained using the artificial intelligence technique of Double deep Q-learning Networks (reinforcement learning) to obtain a complete 3D mesh of the target.

关键词： reinforcement learning deep Q-learning navigation 3D reconstruction monocular RGB camera surface reconstruction point clouds perimeter mapping UAV computer vision

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Implementing W-Net deep learning for terahertz image enhancement and segmentation

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015246-015246页

作者： Mondal, Shyamal Jampani, Kashyap Raj, R. Akshay Chowdhury, Dibakar Roy Sethi, Abhijit Def Inst Adv Technol Dept Appl Phys Pune 411025 India Anurag Univ Ghatkesar Hyderabad 500088 India

Terahertz images typically suffer from poor image quality and do not allow traditional machine learning methods to detect and identify objects. In this work, an image segmentation algorithm called W-Net is deployed for the first time, to segment concealed objects from the poor-quality Terahertz image samples. This involves developing an image enhancement and segmentation system for terahertz images. The proposed system comprises of two stages: in the first stage, the resolution of low resolution THz images has been enhanced by training a U-Net deep learning model. In the latter stage, another U-Net model is trained to segment the enhanced images using corresponding masks to identify regions of interest. The proposed system is evaluated using a dataset of low-resolution terahertz images with its respective high resolution images and masks. The system performed well with an accuracy, precision, receiver operating characteristics and F1 scores of 99.1%, 0.9143, 0.9977 and 0.9146, respectively for enhancement. Moreover, values of the same evaluation metrics deduced as 99.9%, 0.9851, 0.9999 and 0.9923, respectively for segmentation. Therefore, the demonstrated scheme is expected to identify and classify objects hidden in real-time terahertz images and improve the quality of the same to enable accurate segmentation for applications like security screening, biomedical applications, quality and health monitoring in food industry, etc.

关键词： terahertz image processing segmentation enhancement machine learning

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An Empirical Study on Teaching Management and Quality Feedback Based on Human Behavior Detection

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2024年第1期36卷

作者： Zhang, Qian Osman, Siti Zuraidah Md Li, Hongming Lin, Xiao Univ Sains Malaysia Sch Educ Studies Minden Malaysia Univ Florida Coll Educ Gainesville FL USA Shandong Univ Arts Sch Int Art Exchange Jinan Peoples R China

In the current education field, the assessment of teaching management quality mostly relies on subjective judgment and static data, and lacks a real-time and dynamic feedback mechanism. In this study, we propose a deep learning-based human behavior analysis method, which aims to assess teaching management quality in real time by analyzing the behaviors of teachers and students in the classroom. First, in order to detect individual students in the video stream, an augmented detection framework based on YOLO v5s is introduced to process and analyze human actions and interaction patterns in the video data. Immediately after that, we design a channel residual decoupled convolutional neural network to recognize the different states of students. Teaching management quality is assessed by detecting students' classroom attention scores. By conducting experiments in different disciplines and teaching management environments to collect and train the model, the results show that the method can effectively improve the objectivity and accuracy of teaching management quality assessment.

关键词： deep learning Object Detection Behavior Recognition Teaching Management Quality image processing

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