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International Conference on Computing and Networking Technology (ICCNT)

作者： Rizvee Hassan Prito Md. Shafayat Hossain Md. Shajibul Islam Mehzabin Meem Md. Nawab Yousuf Ali Computer Science and Engineering Dept East West University Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface.

关键词： Location awareness Pedestrians Head Roads image processing Object detection real-time systems Safety Object recognition Vehicles

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Energy-efficient real-time visual image adversarial generation and processing algorithm for new energy vehicles

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JOURNAL OF real-time image processing 2024年第5期21卷 161页

作者： Li, Yinghuan Liu, Jicheng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

With the rapid development of deep learning in the last decade, generating and processing real-time images have become one of critical methods in intelligent driving systems for new energy vehicles. However, the real-time images captured by sensors are susceptible to variations in various environments, including different weather and lighting conditions. To enhance the real-time image generation performance for new energy vehicles in complex environments, and improve real-time visual image processing capabilities, this study proposes an energy-efficient real-time visual image adversarial generation and processing algorithm, called as ENV-GAN. It hypothesizes a shared latent domain among mixed image domains after analyzing driving situations under various weather and lighting conditions. Mappings are established between different image domains. Besides, a multi-encoder weight-sharing technique is utilized to enhances the generative adversarial network model. Additionally, the algorithm integrates an attention module to enhance the model & acirc;(TM) s image generation. Experimental results and analysis demonstrate that the new algorithm outperforms existing algorithms in tasks such as defogging, rain removal, and lighting enhancement, offering high energy efficiency and low energy consumption.

关键词： New energy vehicles Generative adversarial network image processing Intelligent driving Energy efficiency

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A Systematic Review of real-time deep learning Methods for image-Based Cancer Diagnostics

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JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2024年 17卷 4411-4425页

作者： Sriraman, Harini Badarudeen, Saleena Vats, Saransh Balasubramanian, Prakash Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

deep learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for realtime cancer diagnosis is explored in depth in this work. real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

关键词： artificial intelligence AI machine learning DL CNN healthcare real-time diagnosis classification image processing elastography feedforward neural network

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A real-time traffic sign detection in intelligent transportation system using YOLOv8-based deep learning approach

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SIGNAL image AND VIDEO processing 2024年第8-9期18卷 6103-6113页

作者： Tang, Mingdeng Chongqing Vocat Inst Safety & Tech Dept Network & Informat Secur Chongqing 404120 Peoples R China

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD.

关键词： Traffic sign detection deep learning YOLOv8 model real-time Intelligent transportation

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Uncertainty-Aware real-time Visual Anomaly Detection With Conformal Prediction in Dynamic Indoor Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4468-4475页

作者： Saboury, Arya Uyguroglu, Mustafa Kemal Eastern Mediterranean Univ Dept Elect & Elect Engn TR-99628 Mersin Turkiye

This letter presents an efficient visual anomaly detection framework designed for safe autonomous navigation in dynamic indoor environments, such as university hallways. The approach employs an unsupervised autoencoder method within deep learning to model regular environmental patterns and detect anomalies as deviations in the embedding space. To enhance reliability and safety, the system integrates a statistical framework, conformal prediction, that provides uncertainty quantification with probabilistic guarantees. The proposed solution has been deployed on a real-time robotic platform, demonstrating efficient performance under resource-constrained conditions. Extensive hyperparameter optimization ensures the model remains dynamic and adaptable to changes, while rigorous evaluations confirm its effectiveness in anomaly detection. By addressing challenges related to real-time processing and hardware limitations, this work advances the state-of-the-art in autonomous anomaly detection. The probabilistic insights offered by this framework strengthen operational safety and pave the way for future developments, such as richer sensor fusion and advanced learning paradigms. This research highlights the potential of uncertainty-aware deep learning to enhance safety monitoring frameworks, thereby enabling the development of more reliable and intelligent autonomous systems for real-world applications.

关键词： Anomaly detection Autoencoders image reconstruction Robots Training Safety Uncertainty Reliability real-time systems Probabilistic logic deep learning for visual perception probability and statistical methods anomaly detection conformal prediction

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The analysis of Iris image acquisition and real-time detection system using convolutional neural network

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JOURNAL OF SUPERCOMPUTING 2024年第4期80卷 4500-4532页

作者： Liu, Yanru Xu, Jiali Yee, Austin Lin Inner Mongolia Univ Sci & Technol Baotou Med Coll Baotou 014040 Peoples R China Shanghai Univ Finance & Econ Sch Math Shanghai 200433 Peoples R China Med & Technol Co Ltd Chengdu 613000 Peoples R China

The purpose is to explore the effect of iris image acquisition and real-time detection systems based on Convolutional Neural Network (CNN) and improve the efficiency of iris real-time detection. Based on existing iris data acquisition and detection systems, this study uses the light field focusing algorithm to collect iris data in live, introduces CNN in deep learning (DL) algorithm, and designs an iris image acquisition and live detection system based on CNN. Afterward, Radial Basis Function (RBF)-Support Vector Machines (SVM) algorithm is used to classify iris feature information. Based on Field Programmable Gate Array (FPGA), a system for iris image acquisition, processing, and display is constructed. Finally, the performance of the constructed system and algorithm are analyzed through simulation experiments. The research results show that the proposed algorithm can automatically select the qualified iris images in live, significantly improve the recognition accuracy of the whole iris recognition system, and the average time of live quality evaluation for each frame image is less than 0.05 s. The focal point of the investigation involves the exploration of a CNN-based iris image acquisition and real-time detection system, with an emphasis on enhancing the efficiency of real-time iris detection. The innovation of this research lies in the integration of deep learning algorithms and light-field focusing techniques, applied to the reconstruction of a FPGA system. Further, the proposed algorithm is compared with Super-Resolution Using Very deep Convolutional Networks (VDSR), deeply Recursive Convolutional Network (DRCN), Residual Dense Network (RDN), and Bicubic. The comparison analysis suggests that the recognition accuracy of the proposed algorithm is the highest, close to 100%. Additionally, the proposed algorithm is compared with the image Quality Evaluation-based Algorithm (IQA) and the Feature Extraction-based Algorithm (FEA), showing that the proposed RBF-SVM

关键词： deep learning Convolutional neural network Iris image acquisition real-time detection Recurrent neural network Smart city

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Application of an Effective Hierarchical deep-learning-Based Object Detection Model Integrated with image-processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

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Future Internet 2023年第10期15卷 322-322页

作者： Chung, Yao-Liang Department of Communications Navigation and Control Engineering National Taiwan Ocean University Keelung City202301 Taiwan

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement

关键词： Object detection

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real-time Facial Expression Recognition Based on image processing in Virtual reality

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-16页

作者： Gong, Qingzhen Liu, Xuefang Ma, Yongqiang Jining Normal Univ Sch Phys & Elect Informat Engn Ulanqab 012000 Inner Mongolia Peoples R China Jingdezhen Univ Sch Informat Engn Jingdezhen 333000 Jiangxi Peoples R China Jining Normal Univ Sch Comp & Big Data Ulanqab 012000 Inner Mongolia Peoples R China

More virtual reality (VR) scenarios have become more prevalent in recent years. More and more people are getting into VR, meaning that objective physiological measures to assess a user's emotional state automatically are becoming more critical. Individuals' emotional states impact their behaviour, opinions, emotions, and decisions. They may be used to analyze VR experiences and make systems react to and engage with the user's emotions. VR environments require users to wear head-mounted displays (HMDs), blocking off their upper faces. That makes traditional Facial Expression Recognition (FER) approaches very limited in their usefulness. Thus, a deep learning (DL) solution combined with image processing is utilized to classify universal emotions: sadness, happiness, disgust, anger, fear and surprise. Hence, this paper suggests the deep Automatic Facial Expression Recognition Model (DAFERM) for interactive virtual reality (VR) applications such as intelligent education, social networks, and virtual training. Two main parts comprise the system: one that uses deep neural networks (DNNs) for facial emotion identification and another that automatically tracks and segments faces. The system begins by following a marker on the front of the head-mounted display (HMD). With the help of the spatial data that has been retrieved, the positions and rotations of the face are estimated to segment the mouth. Finally, the system interacts with DNN using the pixels processed by the lips. It obtains the facial expression results in real time using an adaptive method for histogram-based mouth segmentation.

关键词： Facial expression recognition deep learning Virtual reality image processing deep neural network

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Integrated deep learning and image processing method for modeling energy loss due to shadows in solar arrays

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Solar Energy 2025年 297卷

作者： Araji, Mohamad T. Waqas, Ali School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

Shading poses a serious challenge to photovoltaic (PV) systems power generation, causing energy losses of up to 40 %, leading to power mismatches, hotspot formation, and accelerated module degradation. Accurate modelling and simulation of shading are critical for improving photovoltaic (PV) performance. This study develops two shadow‑detection pipelines: (i) the Classic Hough Transform (CHT) combined with K‑means segmentation and (ii) a new deep Hough Transform (DHT) that learns semantic line features without the need for PV‑specific training data. A 1-kilowatt capacity solar array with planned shading devices was developed and used to perform the experimental analysis. The proposed methodology achieved an accuracy of 0.85, indicating a 32.81 % improvement in solar array detection compared to CHT methods. Computed energy losses due to shading were within 0.5 % to 1.9 % of System Advisor Model (SAM's) simulated losses. Evaluation with transient shadows from a pedestrian, vehicle, and cloud showed an average mIoU of 81.8 % highlighting the methods advantage over existing 3D modeling-based simulation software. Statistical analysis confirmed the method's consistency, yielding Dice = 0.857 (95 % CI 0.728–0.943) and mIoU = 0.771 (CI 0.595–0.893). The parametric analysis highlighted the time of day and number of obstructions as key factors influencing shading on solar arrays, with mornings and evenings experiencing over 6 % shading loss variations. Overall, this integrated approach develops robust, real-time modelling and simulation for optimizing large solar energy systems. © 2025 The Authors

关键词： Semantic Segmentation

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A Hybrid deep learning and Improved SVM Framework for real-time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization

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SENSORS 2025年第7期25卷 2061-2061页

作者： Chen, Jianqiu Xiong, Huan Zhou, Shixuan Wang, Xiang Lou, Benxiao Ning, Longtang Hu, Qingwei Tang, Yang Gu, Guobin Nanning Univ Guangxi Key Lab Int Join China ASEAN Comprehens Tr Nanning 530200 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Intelligent Transportat Syst ITS Guilin 541004 Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Naresuan Univ Fac Logist & Digital Supply Chain Phitsanulok 65000 Thailand

Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.

关键词： image recognition personnel detection ISVM deep learning image preprocessing PCA

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