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LV-YOLO: logistic vehicle speed detection and counting using deep learning based YOLO network

SIGNAL image AND VIDEO processing 2024年第10期18卷 7419-7429页

作者： Rani, N. Gopika Priya, N. Hema Ahilan, A. Muthukumaran, N. PSG Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India PSG Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Commun Engn Tirunelveli Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India

In the era of smart cities and advancing transportation technologies, predicting logistic vehicle and vehicle speed is pivotal to enhancing traffic management, safety, and overall transportation efficiency. Properly predicting vehicle and vehicle speed is critical to the interests of both road users and traffic authorities. However, accurately predicting the vehicle speed and logistics vehicle of a single trip is a difficult task. In some cases, unpredicted accidents will happen, so death cases will increase. To overcome these issues, a novel Logistic Vehicle speed detection using the YOLO (LV-YOLO) method has been introduced to detect logistical vehicles and speed using the YOLO network. The proposed framework is divided into three layers such as image acquisition, segmentation layer, and detection layer. In the image acquisition layer, a CCTV camera captures highway traffic video. The collected video is converted into frames. In the segmentation layer, the video frame is segmented using U-Net, which segments the vehicle in the video frames. The detection layer performs truck detection, and speed detection using LV-YOLO on segmented frames based on the Boxy Vehicle dataset. The simulated results show that the LV-YOLO technique maintains excellent mAP levels of 99.42%. The LV-YOLO improves the overall mAP by 1.72, 5.42, and 0.82% better than the Simple Vehicle Counting System, real-time Detection, and Advance YOLOv3 Model for vehicle detection, 4.81, and 2.63% better than deep learning and CAN protocol, and 1D-CNN speed estimation mode for speed prediction respectively.

关键词： Vehicle speed Detection deep learning U-Net YOLO

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real-time Pedestrian Detection in Hazy Weather for Surveillance Systems 8

Real-Time Pedestrian Detection in Hazy Weather for Surveilla...

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8th IEEE International Conference on image and Signal processing and their Applications, ISPA 2024

作者： Yassine, Bouafia Larbi, Guezouli Lakhlef, Hicham University of Batna2 Algeria LaSTIC Laboratory Computer Science Department Batna Algeria Environment and Sustainable Development LEREESI Laboratory Higher National School of Renewable Energy Batna Algeria Sorbonne University University of Technology of Compiègne Compiègne France

ISBN: (纸本)9798350309249

real-time pedestrian detection is an expanding research topic that is crucial in various vision-based applications. Effective detection of pedestrians in various environments and situations will significantly improve human safety. However, the performance of current pedestrian detection algorithms faces challenges in scenarios with reduced visibility and blurred appearance, such as hazy weather conditions. This study addresses this issue by seeking an optimal balance between accuracy and speed for deploying deep learning-based pedestrian detection in real-time intelligent surveillance systems. For this aim, we used the transfer learning technique to fine-tune small versions of state-of-the-art YOLO models (YOLONAS-small, YOLOv8-small, YOLOv6-small, and YOLOv5-small) to perform pedestrian detection in hazy weather effectively. Experiment results demonstrate the effectiveness of our methods in accurately detecting pedestrians under hazy conditions by achieving high average precision and F-Score. Moreover, they all operate in real time, with impressive processing speeds ranging from 52.41 to 186.22 FPS. © 2024 IEEE.

关键词： deep learning

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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition

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SIGNAL image AND VIDEO processing 2025年第5期19卷 1-18页

作者： Mohana, M. Subashini, P. Ghinea, George Avinashilingam Inst Ctr Machine Learning & Intelligence Dept Comp Sci Coimbatore India Brunel Univ Dept Comp Sci London England

Facial expression recognition (FER) continues to be a vibrant research field, driven by the increasing need for its practical applications in areas such as e-learning, healthcare, candidate interview analysis, and more. Most deep learning approaches in supervised FER systems heavily rely on large, labeled datasets. Implementing FER in Convolutional Neural Networks (CNNs) often requires many layers, leading to extended training times and difficulties in finding optimal parameters. This can result in challenges in creating distinct facial expression patterns for classification, leading to poor real-time emotion classification In this paper, we propose a novel approach known as the deep Semi-supervised Convolutional Sparse Autoencoder to address the aforementioned issues and enhance FER performance and prediction accuracy. This approach comprises two parts: (i) Initially, a deep convolutional sparse autoencoder is trained with unlabeled samples of facial expressions. Here, sparsity is introduced in the convolutional block to enforce penalties, focusing on more relevant features for feature representation in the latent space. (ii) A trained encoder with a feature map is connected to a fully connected layer with softmax for final fine-tuning with learned weights and labeled facial expression samples in a semi-supervised approach for emotion classification. This approach was experimented with two benchmark datasets, namely CK + and JAFFE, and achieved significant results of 98.98% and 93.10% accuracy, respectively. The results were analyzed using established state-of-the-art techniques. Additionally, eXplainable AI (XAI) methods like Grad-CAM and image-LIME were employed to interpret the performance and prediction outcomes of the DSCSA model.

关键词： Autoencoder Convolutional sparse autoencoder Facial expression recognition Feature representation Unsupervised learning Semi-supervised learning

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Leveraging Extended Windows in End-to-End deep learning for Improved Continuous Myoelectric Locomotion Prediction

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1170-1179页

作者： Lin, Yuzhou Zhang, Yuyang Zhong, Wenjuan Xiong, Wenxuan Xi, Zhen Chen, Yi-Feng Zhang, Mingming Southern Univ Sci & Technol Dept Biomed Engn Shenzhen Key Lab Smart Healthcare Engn Guangdong Prov Key Lab Adv Biomat Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Coll Engn Shenzhen 518055 Peoples R China

Current surface electromyography (sEMG) methods for locomotion mode prediction face limitations in anticipatory capability due to computation delays and constrained window lengths typically below 500 ms-a practice historically tied to stationarity requirements of handcrafted feature extraction. This study investigates whether end-to-end convolutional neural networks (CNNs) processing raw sEMG signals can overcome these constraints through extended window lengths (250 ms to 1500 ms). We systematically evaluate six window lengths paired with three prediction horizons (model forecasts 50 ms to 150 ms ahead) in a continuous locomotion task involving eight modes and 16 transitions. The optimal configuration (1000 ms window with 150 ms horizon) achieved subject-average accuracies of 96.93% (steady states) and 97.50% (transient states), maintaining 95.03% and 85.53% respectively in real-time simulations. With a net averaged anticipation time of 147.9 ms after 2.1 ms computation latency, this approach demonstrates that windows covering 74% of the gait cycle can synergize with deep learning to balance the inherent trade-off between extracting richer information and maintaining system responsiveness to changes in activity.

关键词： Legged locomotion Accuracy deep learning Stairs Training Sensors real-time systems Delays Convolutional neural networks Testing Surface electromyography locomotion mode anticipatory prediction extended window end-to-end deep learning prediction horizon

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Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 9936-9944页

作者： De Sousa, Kevin Pilikos, Georgios Azcueta, Mario Floury, Nicolas European Space Agcy ESA European Space Res & Technol Ctr ESTEC Radio Frequency Payloads & Technol Div Wave Interact & Propagat SectElect Dept NL-2200 AG Noordwijk Netherlands Univ Toulouse CNRS Ctr Etud Spatiales Biosphare CESBIO CNESIRDINRAEUT3 Toulouse France European Space Agcy ESA European Space Res & Technol Ctr ESTEC Copernicus Sentinel Payload Sect Copernicus Space Component Space SegmentEarth Observat Projects De NL-2200 AG Noordwijk Netherlands

Synthetic aperture radar (SAR) is an indispensable tool for marine monitoring. Conventional data processing involves data down-linking and on-ground operations for image focusing, analysis, and ship detection. These steps take significant amount of time, resulting in potentially critical delays. In this work, we propose a ship detection algorithm that operates directly on raw SAR echoes, based on convolutional neural networks. To evaluate our approach, we performed experiments using raw data simulations and real raw SAR data from Sentinel-1 stripmap mode scenes. Preliminary results on this set show the capability of detecting multiple ships from raw data with similar accuracy as using single-look-complex images as input. Simultaneously, running time is reduced significantly, by-passing the image focusing step. This illustrates the great potential of deep learning, moving toward more intelligent SAR systems.

关键词： Marine vehicles Synthetic aperture radar Data models deep learning image segmentation Focusing European Space Agency raw data ship detection synthetic aperture radar (SAR)

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Wireless channel corrupted image denoising using residual learning of adaptive wavelet with dilated deep convolutional neural network

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SIGNAL image AND VIDEO processing 2024年第3期18卷 2309-2321页

作者： Sreedhar, M. Aparna, K. Jawaharlal Nehru Technol Univ Anantapur Ananthapuramu Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur JNTUA Coll Engn ECE Dept Constituent Coll Kalikiri Ananthapuramu Andhra Pradesh India

In digital image transmission, channel coding is essential for data integrity and accurate reception during digital communication. While minimizing the consequences of the channel, typical communication methods neglect the received context and content-related data. Many significant restrictions are applied to image transmission in digital communication. Due to the limited qualities of transferred data, image quality suffers across over wireless networks. Moreover, the transmission of digital images is a complicated task based on their shape, size, and bandwidth. Hence, it lacks flexibility and practicality in the real-world environment. Different image-denoising techniques are employed to decrease the noisy image channel effects. Thus, this research aims to analyze the images gathered during wireless channel communication to precisely eliminate the bugs and the impact of channel degradation. The image denoising is carried out during the wireless channel communication process at the receiver end. A novel deep Residual learning of Adaptive Wavelet with Dilated deep Convolutional Neural Network (RL-AWDDCNN) method is used to denoise the image more effectively. Hence, the residual images are generated based on the heuristic concept, where an improved flow direction algorithm (IFDA) is developed for optimizing the wavelet parameters with Dilated deep Convolutional Neural Network. Throughout the result analysis, the designed method scored a 3037.233% peak signal-to-noise ratio (PSNR) rate and an 80.18675% structural similarity index measure (SSIM) rate. Thus, the developed denoising model's performance and the experimental results are analyzed and compared with various existing methods concerning the standard image quality measures.

关键词： image denoising Dilated deep convolutional neural network Improved flow direction algorithm Discrete wavelet transform image decomposition

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Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data

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IEEE ACCESS 2024年 12卷 82706-82719页

作者： Wang, Songsong Xu, Ouguan Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Peoples R China Ocean Univ China Coll Engn Qingdao 266100 Peoples R China

Due to the many disaster-causing factors of mountain torrents, rapid changes in time and space, and rapid disasters, the traditional hydrological models and general data-driven mountainous flood forecasting models in small watershed areas have low accuracy and timeliness. Therefore, this paper proposed the time series Recurrent Neural Networks (RNN) and data processing for small mountain flood level forecasting, it includes base RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model. We use the Tiaoxi catchment's mountain flood data including hydrology, meteorology, geography and human activities at Zhejiang province of China. We construct multi-dimensional time series data processing and the model test system, and develop multi-dimensional disaster factor data on an hourly scale to suit the training and prediction of deep learning (DL)'s structure. The experiment shows that time series base RNN, LSTM and GRU models can be considered as the efficient hydrology modeling approach. The LSTM model demonstrated the best accuracy and real-time in the study area.

关键词： Mountain flood forecasting small watersheds RNN LSTM multi-dimensional data deep learning

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Consensus-Agent deep Reinforcement learning for Face Aging

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IEEE TRANSACTIONS ON image processing 2024年 33卷 1795-1809页

作者： Lin, Ling Liu, Hao Liang, Jinqiao Li, Zhendong Feng, Jiao Han, Hu Ningxia Univ Sch Informat Engn Yinchuan 750021 Peoples R China Ningxia Key Lab Artificial Intelligence & Informat Yinchuan 750021 Peoples R China Ningxia Univ Sch Innovat & Entrepreneurship Yinchuan 750021 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Face aging tasks aim to simulate changes in the appearance of faces over time. However, due to the lack of data on different ages under the same identity, existing models are commonly trained using mapping between age groups. This makes it difficult for most existing aging methods to accurately capture the correspondence between individual identities and aging features, leading to generating faces that do not match the real aging appearance. In this paper, we re-annotate the CACD2000 dataset and propose a consensus-agent deep reinforcement learning method to solve the aforementioned problem. Specifically, we define two agents, the aging process agent and the aging personalization agent, and model the task of matching aging features as a Markov decision process. The aging process agent simulates the aging process of an individual, while the aging personalization agent calculates the difference between the aging appearance of an individual and the average aging appearance. The two agents iteratively adjust the matching degree between the target aging feature and the current identity through a form of synergistic cooperation. Extensive experimental results on four face aging datasets show that our model achieves convincing performance compared to the current state-of-the-art methods.

关键词： Face aging deep reinforcement learning Markov decision process

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deep learning-based Metal Extraction with No Reference in Dental CBCT for Metal Artifact Reduction

Deep Learning-based Metal Extraction with No Reference in De...

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Conference on Medical Imaging - image processing

作者： Lee, Hyunwoo Kim, Sunjung Cho, Min Kook Osstem Implant Co Ltd Imaging R&D Ctr Seoul 07789 South Korea

ISBN: (纸本)9781510671577;9781510671560

Metal artifact reduction (MAR) is crucial for improving the quality of dental cone-beam computed tomography (CBCT) images. However, inaccurate extraction of metal in MAR can lead to incomplete suppression of metal artifacts, obscuring adjacent bone structures in resultant images. The pre-existing metal artifacts such as streaks, shadowing, and cupping complicate the extraction of metallic segments. In particular, dental CBCT applications are vulnerable to metal artifacts due to the lack of anti-scatterers and the frequent appearance of metallic restorations. Conventional image processing methods for metal extraction have some limitations, for example, relying on predefined parameters and requiring manual interventions. To overcome these challenges, we introduce a deep learning-based metal extraction method in an unsupervised manner, eliminating the labor-intensive label annotation process. This method combines several methods, including unpaired image-to-image translation, weakly supervised learning, and geometric transformations, followed by supervised learning for real-time metal extraction. As a result, the proposed method achieves high-quality metal extraction with an accuracy of 90%, as evaluated with manual annotations, and shows a significant improvement in MAR performance for clinical CBCT images compared to our conventional method.

关键词： cone-beam computed tomography metal artifact reduction metal extraction deep learning

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Multi-Modal Comparative Analysis on Execution of Phishing Detection Using Artificial Intelligence

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IEEE ACCESS 2024年 12卷 163016-163041页

作者： Dsouza, Divya Jennifer Rodrigues, Anisha P. Fernandes, Roshan NITTE Deemed Univ NMAM Inst Technol Dept Comp Sci & Engn Nitte 574110 India NITTE Deemed Univ NMAM Inst Technol Dept Cyber Secur Nitte 574110 India

Phishing is the process of deceiving or stealing private or confidential information through illicit means. This could lead to financial loss, loss of reputation, and identity theft. Hence, identifying and preventing the use of such phishing sites becomes crucial. In data science, the term outlier, also termed an anomaly refers to points or series of data that opt out of the normal behaviour of the system under study. Anomaly detection touches down on the concepts related to studying the authentic outlier in a data set. The paper aims to present the optimised techniques and multiple modes of executing the process for detecting phishing websites. The most relevant features are chosen for execution by applying feature extraction. The Mendley phishing websites dataset is used to detect phishing websites, along with the SPAM-HAM publicly available dataset, which is used for detecting SPAM/HAM classification for SMS data in this research study. The experiments are also carried out on a custom dataset to avoid any bias present in a publicly available dataset. The study is carried out in three modes, namely offline, batch, and incremental, using machine learning models. The performance evaluation metrics such as accuracy, f1 score, precision, recall, and time complexity of the machine learning models and accuracy and loss metrics of the deep learning models are compared between the different modes. The study is then summarised by detailing the pros and cons of each of the modes and models used for the study. The incremental mode of execution suits better for real-time processing, with an accuracy of 97.1% on the custom dataset using the adaptive random forest (ARF) classifier available in the Python's River Framework. But if we make use of the deep learning approach with Keras sequential model, the accuracy obtained was 99.28%.

关键词： Phishing Feature extraction Accuracy Uniform resource locators Message services Electronic mail real-time systems deep learning Anomaly detection Streams Outlier detection anomaly phishing incremental learning feature extraction batch processing spam machine learning

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