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

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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real-time Large-Motion Deblurring for Gimbal-Based Imaging Systems

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL processing 2024年第3期18卷 346-357页

作者： Varghese, Nisha Rajagopalan, A. N. Ansari, Zahir Ahmed Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India IRDE DRDO Dehra Dun 248001 India

Robotic systems employed in tasks such as navigation, target tracking, security, and surveillance often use camera gimbal systems to enhance their monitoring and security capabilities. These camera gimbal systems undergo fast to-and-fro rotational motion to surveil the extended field of view (FOV). A high steering rate (rotation angle per second) of the gimbal is essential to revisit a given scene as fast as possible, which results in significant motion blur in the captured video frames. real-time motion deblurring is essential in surveillance robots since the subsequent image-processing tasks demand immediate availability of blur-free images. Existing deep learning (DL) based motion deblurring methods either lack real-time performance due to network complexity or suffer from poor deblurring quality for large motion blurs. In this work, we propose a Gyro-guided Network for real-time motion deblurring (GRNet) which makes effective use of existing prior information to improve deblurring without increasing the complexity of the network. The steering rate of the gimbal is taken as a prior for data generation. A contrastive learning scheme is introduced for the network to learn the amount of blur in an image by utilizing the knowledge of blur content in images during training. To the GRNet, a sharp reference image is additionally given as input to guide the deblurring process. The most relevant features from the reference image are selected using a cross-attention module. Our method works in real-time at 30 fps. As a first, we propose a Gimbal Yaw motion real-wOrld (GYRO) dataset of infrared (IR) as well as color images with significant motion blur along with the inertial measurements of camera rotation, captured by a gimbal-based imaging setup where the gimbal undergoes rotational yaw motion. Both qualitative and quantitative evaluations on our proposed GYRO dataset, demonstrate the practical utility of our method.

关键词： Robotic systems gimbal-based imaging real-time motion deblurring gyro-aided motion deblurring video surveillance reference-based motion deblurring contrastive learning Robotic systems gimbal-based imaging real-time motion deblurring gyro-aided motion deblurring video surveillance reference-based motion deblurring contrastive learning

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image processing framework for in-process shaft diameter measurement on legacy manual machines

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期135卷 4323-4338页

作者： Choudhari, Sahil J. Singh, Swarit Anand Kumar, Aitha Sudheer Desai, Kaushal A. Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur 342030 Rajasthan India

In-process dimension measurement is critical to achieving higher productivity and realizing smart manufacturing goals during machining operations. Vision-based systems have significant potential to serve for in-process dimensions measurements, reduce human interventions, and achieve manufacturing-inspection integration. This paper presents early research on developing a vision-based system for in-process dimension measurement of machined cylindrical components utilizing image-processing techniques. The challenges with in-process dimension measurement are addressed by combining a deep learning-based object detection model, You Only Look Once version 2 (YOLOv2), and image processing algorithms for object localization, segmentation, and spatial pixel estimation. An automated image pixel calibration approach is incorporated to improve algorithm robustness. The image acquisition hardware and the real-time image processing framework are integrated to demonstrate the working of the proposed system by considering a case study of in-process stepped shaft diameter measurement. The system implementation on a manual lathe demonstrated robust utilities, eliminating the need for manual intermittent measurements, digitized in-process component dimensions, and improved machining productivity.

关键词： Dimension measurement In-process inspection Machine vision image processing Object segmentation deep learning

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deep-learning accelerated super-resolution radial fluctuations (SRRF) enables real-time live cell imaging

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OPTICS AND LASERS IN ENGINEERING 2024年 172卷

作者： Chen, Jincheng Fang, Qiuyu Huang, Li Ye, Xin Jin, Luhong Zhang, Heng Luo, Yinqian Zhu, Min Zhang, Luhao Ji, Baohua Tian, Xiang Xu, Yingke Zhejiang Univ Dept Biomed Engn State Key Lab Extreme Photon & Instrumentat Zhejiang Prov Key Lab Cardiocerebral Vasc Detect T Hangzhou 310027 Peoples R China Zhejiang Univ Binjiang Inst Hangzhou 310053 Peoples R China Zhejiang Univ Dept Engn Mech Biomech & Biomat Lab Hangzhou 310027 Peoples R China Zhejiang Univ Childrens Hosp Natl Clin Res Ctr Childrens Hlth Dept EndocrinolSch Med Hangzhou 310051 Peoples R China

The Super-resolution radial fluctuations (SRRF) algorithm analyzes radial and temporal fluorescence intensity fluctuations in an image sequence, which typically includes tens or hundreds of raw images to generate one super-resolution image. At present, most of the SRRF applications rely on large amounts of raw images or tedious post-processing and computation, thus are not feasible for real-time live cell super-resolution imaging. Here, we developed a novel deep learning accelerated SRRF method, which significantly reduced the requirement of raw images for super-resolution reconstruction. Our results showed that by using only 5 low signal-to-noise ratio (SNR) images, we were able to achieve super-resolution SRRF reconstruction to a comparable resolution as the traditional method. We demonstrated that by integration of GPU computing and the sliding window reconstruction method, the dynamic contraction of microtubules and the interactions between microtubules and clathrin-coated pits (CCPs) can be visualized in real-time with super-resolution. In summary, we established the deep learning accelerated SRRF method, which permits real-time, long-term and multi-color live cell super -resolution imaging, and we anticipate it will have vast biomedical applications.

关键词： Super -resolution imaging deep learning SRRF reconstruction TIRFM U -Net

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Fast no-reference deep image dehazing

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MACHINE VISION AND APPLICATIONS 2024年第5期35卷 122页

作者： Qin, Hongyi Belyaev, Alexander G. Imperial Coll London Dept Elect & Elect Engn London England Heriot Watt Univ Sch Engn & Phys Sci Inst Sensors Signals & Syst Edinburgh Scotland

This paper presents a deep learning method for image dehazing and clarification. The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al. in IEEE Trans Pattern Anal Mach Intell 44(8):4225-4238, 2021) for the image dehazing problem and uses high-order curves to adjust the dynamic range of images and achieve dehazing. Training the proposed dehazing neural network does not require paired hazy and clear datasets but instead utilizes a set of loss functions, assessing the quality of dehazed images to drive the training process. Experiments on a large number of real-world hazy images demonstrate that our proposed network effectively removes haze while preserving details and enhancing brightness. Furthermore, on an affordable GPU-equipped laptop, the processing speed can reach 1000 FPS for images with 2K resolution, making it highly suitable for real-time dehazing applications.

关键词： image dehazing and clarification No-reference neural network training real-time dehazing Zero-DCE

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Cross-task cognitive workload estimation using eye tracking

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SIGNAL image AND VIDEO processing 2025年第5期19卷

作者： Yang, Lin Wang, Lei Xu, Wenchang Wang, Biao Ren, Hanbin Yang, Aijuan Univ Sci & Technol China Sch Biomed Engn Suzhou Div Life Sci & Med Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol CAS Key Lab Biomed Diagnost Suzhou 215163 Peoples R China Suzhou ZhongKe Med Device Ind Dev Co Ltd Suzhou 215163 Peoples R China Jinan Guoke Med Technol Dev Co Ltd Jinan 250001 Peoples R China

Cognitive workload is a key factor in understanding human cognitive performance, especially in scenarios that require intensive information processing. This study introduces an innovative method to estimate cognitive workload using eye-tracking data and proposes a novel deep learning model called BiTCADNet (Bidirectional Temporal Convolutional self-Attention Dense Network). Experiments using the newly created dataset "Cognitive-Eye-Movement" and the publicly available dataset "CL-Drive" show that BiTCADNet significantly outperforms traditional deep learning models in terms of accuracy, precision, recall, and F1 scores are significantly better than traditional machine learning methods. The proposed method provides a more effective way to monitor and evaluate cognitive workload in real-time, opening the way for its applications in various human-computer interaction environments.

关键词： deep-learning Cognitive-workload Eye-tracking Self-attention mechanism

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Identification of Drowning Victims in Freshwater Bodies using Drift Prediction and image processing based on deep learning 10

Identification of Drowning Victims in Freshwater Bodies usin...

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10th International Conference on Advances in Computing and Communications, ICACC 2021

作者： Unnikrishnan, Anjana Roshni, A.T. Anusha, P.R. Vinny, Anju M Anuraj, C.K. Toc H Institute of Science Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9781665439190

Year after year drowning deaths are increasing tremendously, making it the 3rd leading cause of unintentional injury deaths worldwide. Drift prediction methodology is typically not used in river ecosystems and conventional methods for human rescue do not account for feasible and faster human detection. Utilization of multiple sensor data in underwater human rescue applications can capacitate faster human detection. This paper discusses the design, implementation, and testing of such an underwater human detection system, which spots the victim drifting or drowning in freshwater ecosystems. The water flow sensor attached to this portable device can calculate drift distance to track down the victim. The ultrasonic sensor activates the underwater camera upon detecting an object, to facilitate real-time human localization. We performed real-time object detection on a custom dataset by applying DarkNet-53 pre-trained weights on YOLOv3 architecture and a mean Average Precision (mAP) of 98.0% was achieved. The system attained a detection depth of 5m. Combined action of drift distance calculator and YOLOv3 real-time detection model can speed up underwater human extrication. © 2021 IEEE.

关键词： Object detection

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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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