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Research on Parking Space Detection and Prediction Model Based on CNN-LSTM

IEEE ACCESS 2024年 12卷 30085-30100页

作者： Xu, Zhuye Tang, Xiao Ma, Changxi Zhang, Renshuai Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou 730070 Peoples R China Changsha Univ Sci & Technol Natl Engn Res Ctr Highway Maintenance Technol Changsha 410114 Hunan Peoples R China Lanzhou Jiaotong Univ Sch New Energy & Power Engn Intelligent Bldg & Bldg Automat Lab Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Transportat Lanzhou 730070 Peoples R China

With the continuous acceleration of urbanization, the parking problem is becoming increasingly serious. How to better manage parking resources has become an urgent problem to be solved in urban development. In this context, according to the historical data and real-time video data collected by the parking camera, this paper proposes an algorithm for parking space detection and state recognition. Through image preprocessing, region of interest selection, Hough line detection, and parking information recognition of the input test image, an intelligent parking space detection model is constructed, which improves the utilization rate of parking space and reduces the management cost. On this basis, according to the free parking space data obtained by the detection algorithm, a short-term demand prediction algorithm for on-road parking based on Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM) was proposed. Through the preprocessing of input parking space data, time vector transformation, data separation, model training, and prediction, the parking demand data is predicted and analyzed. By comparing the prediction results of multiple models, it was found that the CNN-LSTM prediction model had the best model stability and goodness of fit, the lowest Mean Error (MAE) and Root Mean Square Error (RMSE), the errors of working days were 13.301 and 21.156, and the errors of rest days were 12.573 and 20.739, respectively. It shows that CNN-LSTM can effectively capture the time and spatial feature information of parking lot free parking space data, and the prediction accuracy is good, which can be used to predict the number of free parking spaces in parking lots.

关键词： Automobiles Predictive models Convolutional neural networks real-time systems Gray-scale Urban areas image edge detection deep learning Key words deep learning parking space detection image processing convolutional neural network long short-term memory neural network

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PHL-YOLO: a real-time lightweight yarn inspection method

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JOURNAL OF real-time image processing 2025年第1期22卷 1-14页

作者： Dai, Jiachao Ren, Jia Li, Shangjie Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Changshan Res Inst Co Ltd Quzhou 324299 Peoples R China

To meet the real-time and high-precision detection requirements in actual yarn production, this paper introduces a novel lightweight yarn detection method named PHL-YOLO, an optimized version of the YOLOv8 algorithm. Initially, we developed the PEC2F module by fusing PConv (partial convolution) and EMA (efficient multi-scale attention) components to enhance the model's feature extraction capabilities while concurrently reducing computational demands and the number of parameters. Subsequently, we deployed the HWD (Haar wavelet downsampling) module for downsampling, which effectively diminishes image resolution without sacrificing the critical information necessary for precise detection. Additionally, to address the excessive parameters and computational complexity in the detection head of YOLOv8, we introduced the LSCDH (lightweight shared convolutional detection head) module, which significantly simplifies the structure of the detection head. Finally, we applied a structured pruning technique, GSPMD (group-level structured pruning method with DepGraph), to further refine the model. The experimental outcomes indicate that our enhanced model PHL-YOLO has achieved a 1.0% increase in precision, a 2.5% improvement in recall, and a 2.3% increase in mAP (mean average precision), along with a 26 FPS performance boost over the original model. Furthermore, our model's computational load and parameter count are only 29.6% and 10.2% of those in the original YOLOv8 model, respectively. PHL-YOLO not only effectively reduces model complexity, but also upholds high performance levels, providing a valuable reference for rapid and accurate yarn detection in real-world production scenarios.

关键词： Yarn inspection deep learning YOLOv8 Lightweight Model pruning

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A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition

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SIGNAL image AND VIDEO processing 2024年第6-7期18卷 5433-5448页

作者： Chandrabanshi, Vishnu Domnic, S. Natl Inst Technol Dept Comp Applicat Tiruchirappalli 620015 Tamil Nadu India

Visual Speech Recognition (VSR) is an appealing technology for predicting and analyzing spoken language based on lip movements. Previous research in this area has primarily concentrated on leveraging both audio and visual cues to achieve enhanced accuracy in speech recognition. However, existing solutions encounter significant limitations, including inadequate training data, variations in speech patterns, and similar homophones, which need more comprehensive feature representation to improve accuracy. This article presents a novel deep learning model for performing word level VSR. In this study, we have introduced a dynamic learning rate scheduler to adapt the learning parameter during model training. Additionally, we employ an optimized Three-Dimensional Convolution Neural Network for extracting spatio-temporal features. To enhance context processing and ensure accurate mapping of input sequences to output sequences, we combine Bidirectional Long Short Term Memory with the CTC loss function. We have utilized the GRID dataset to assess word-level metrics, including Word Error Rate (WER) and Word Recognition Rate (WRR). The model achieves 1.11% WER and 98.89% WRR, respectively, for overlapped speakers. This result demonstrates that our strategy outperforms and is more effective than existing VSR methods. Practical Implications - The proposed work aims to elevate the accuracy of VSR, facilitating its seamless integration into real-time applications. The VSR model finds applications in liveness detection for person authentication, improving password security by not relying on written or spoken passcodes, underwater communications and aiding individuals with hearing and speech impairments in the medical field.

关键词： 3D-CNN BiLSTM Visual speech recognition LRS deep learning

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real-time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing images Using an Acousto-Optic Tunable Filter Spectrometer

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DRONES 2024年第7期8卷

作者： Liu, Hong Hu, Bingliang Hou, Xingsong Yu, Tao Zhang, Zhoufeng Liu, Xiao Liu, Jiacheng Wang, Xueji Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Chinese Acad Sci Key Lab Spectral Imaging Technol Xian 710119 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of similar to 30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.

关键词： acousto-optic tunable filter image registration real-time processing spectral imaging UAV remote sensing

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EdgeScan for IoT Contextual Understanding With Edge Computing and image Captioning

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 6519-6535页

作者： Hafeth, Deema Abdal Al-Khafajiy, Mohammed Kollias, Stefanos Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

The emergence of Edge Computing has shifted the processing capabilities in proximity to the Internet of Things (IoT) data sources, offering solutions to latency and bandwidth constraints applications. This shift complements Cloud Computing, especially in handling real-time data processing and enhancing processing. image processing, particularly image captioning for smart monitoring systems, benefits greatly from this synergy. image captioning plays a crucial role in understanding visual data. While early methods excelled in encoder-decoder frameworks and attention mechanisms, they often overlooked semantic representations which are essential for comprehensive image understanding. To address this gap, we introduce the EdgeScan framework, leveraging Edge Computing for image analysis and semantic feature extractions closer to data sources. EdgeScan integrates visual and semantic features to create more informative and enriched image captions that enhance image captioning accuracy. The EdgeScan image captioning model architecture is capable of 1) learning the salient image region's specific feature representation and 2) co-embedding visual attention and semantic attributes in one space for feature fusion. This improves models' ability to interpret and respond to data in a meaningful way, which is particularly valuable for IoT applications that require a deep understanding of the semantics of diverse and constantly changing data for efficient operation. Extensive experiments were conducted on the MS-COCO dataset to demonstrate the superiority of EdgeScan in both quantitative and qualitative performance, achieving highest consensus-based image description evaluation score of 120.9, as well as notable scores of 78.6 for BLEU@1 and 57.7 for recall-oriented understudy for gisting evaluation metrics, promising advancements in IoT-driven image understanding and competitiveness against the state of the art.

关键词： image edge detection Cloud computing Semantics Computational modeling Edge computing Visualization Data models Feature extraction Data processing image captioning Internet of Things (IoT) semantic attentions semantic attentions

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Hybrid synthetic data generation pipeline that outperforms real data

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JOURNAL OF ELECTRONIC IMAGING 2023年第2期32卷

作者： Natarajan, Sai Abinesh Madden, Michael G. Natl Univ Ireland Galway Sch Comp Sci Galway Ireland

Fine-tuning a pretrained model with real data for a machine learning task requires many hours of manual work, especially for computer vision tasks, where collection and annotation of data can be very time-consuming. We present a framework and methodology for synthetic data collection that is not only efficient in terms of time taken to collect and annotate data, making use of free- and open-source software tools and 3D assets but also beats the state-of-the-art against real data, which is the ultimate test for any similar-to-real approach. We test our approach on a set of image classes from ObjectNet, which is a challenging image classification benchmark test dataset that is designed to be similar in many respects to imageNet but with a wider variety of viewpoints, rotations, and backgrounds, which can make it more difficult for transfer learning problems. The novelty of our approach stems from the way we create complex backgrounds for 3D models using 2D images laid out as decals in a 3D game engine, where synthetic images are captured programmatically with a large number of systematic variations. We demonstrate that our approach is highly effective, resulting in a deep learning model with a top-1 accuracy of 72% on the ObjectNet data, which is a new state-of-the-art result. In addition, we present an efficient strategy for learning rate tuning that is an order of magnitude faster than regular grid search.

关键词： synthetic data ObjectNet computer vision image classification deep learning learning rate

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deep learning-assisted common temperature measurement based on visible light imaging

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Chinese Physics B 2024年第8期33卷 230-236页

作者：朱佳仪何志民黄成曾峻林惠川陈福昌余超群李燕张永涛陈焕庭蒲继雄 College of Physics and Information Engineering Minnan Normal UniversityZhangzhou 363000China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science&EngineeringHuaqiao UniversityXiamen 361021China

real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection *** paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light *** angle and imaging distance do not affect the temperature detection using visible optical images by the ***,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for *** to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

关键词： convolutional neural network visible light image temperature measurement low-to-medium-range temperatures

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deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2024年 117卷 103184-103184页

作者： Ylisiurua, Sampo Sipola, Annina Nieminen, Miika T. Brix, Mikael A. K. Univ Oulu Res Unit Hlth Sci & Technol Oulu 90220 Finland Oulu Univ Hosp Dept Diagnost Radiol Oulu 90220 Finland Univ Oulu Med Res Ctr Oulu 90220 Finland Oulu Univ Hosp Oulu 90220 Finland Oulu Univ Hosp Dept Dent Imaging Oulu 90220 Finland Univ Oulu Res Unit Oral Hlth Sci Oulu 90220 Finland

Purpose: The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods. Methods: Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses. Results: The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods. Conclusion: Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

关键词： Cone -beam computed tomography deep learning image quality

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deep MR parametric imaging with the learned L plus S model and attention mechanism

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IET image processing 2023年第4期17卷 969-978页

作者： Qu, Wenyi Cheng, Jing Zhu, Yanjie Liang, Dong Chinese Acad Sci Shenzhen Inst Adv Technol Res Ctr Med AI Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Peoples R China United Imaging Res Inst Innovat Med Equipment Shenzhen Peoples R China Pazhou Lab Guangzhou Peoples R China

Magnetic resonance (MR) parametric imaging can help the assessment of some certain diseases with its various contrast mechanisms. However, the main issue of MR parametric imaging is the long acquisition time, introducing many problems such as uncomfortable experiences for patients and motion artefacts. With the deep learning methods developing, some unrolling ones have been introduced to MR imaging as solutions. The purpose of this study is to improve both the quality and the speed of parametric imaging. The proposed method, named as LSA-Net, introduced a learned low rank plus sparsity model with attention mechanism to reconstruct highly under-sampled data for MR parametric imaging. The L+S model was used to represent the shared image structure among the parameter-weighted images as low-rank part and the difference among images and the ideal model as sparse part. Besides, the attention block was introduced to effectively improved quality for the region of interest, which was most concerned in clinical applications. Then the parameter map was generated from the reconstructed parameter-weighted images by its exponential model. The LSA-Net was evaluated on the in-vivo 3D T1 rho$T1\rho$ mapping, showing better performance on both image quality and time consumed than the comparing methods including LLR, SCOPE, L+S, and L+S-Net.

关键词： Biology and medical computing magnetic resonance parametric imaging diseases learned low rank plus sparsity model MR parametric imaging learning (artificial intelligence) attention mechanism biomedical MRI Medical magnetic resonance imaging and spectroscopy shared image structure Neural nets Optical, image and video signal processing Computer vision and image processing techniques Biomedical magnetic resonance imaging and spectroscopy image quality s model deep learning (artificial intelligence) deep learning methods contrast mechanisms image reconstruction Patient diagnostic methods and instrumentation reconstructed parameter-weighted images MR imaging medical image processing

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Lightweight deep learning-based automated concrete surface inspection: Crack classification and pixel-level segmentation

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SMART STRUCTURES AND SYSTEMS 2025年第3期35卷 163-175页

作者： Yang, Dong Cai, Yuan-Yuan Xu, En-Dian Zhang, Jing Yuan, Ye Wang, Yan-Jia Guangzhou Univ Earthquake Engn Res & Test Ctr EERTC Guangzhou Peoples R China Guangzhou Univ Key Lab Earthquake Resistance Earthquake Mitigat & Struct Safety Minist Educ Guangzhou Peoples R China Hefei Univ Technol Dept Civil Engn Hefei Anhui Peoples R China Jinan Univ Dept Mech & Construct Engn Guangzhou Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China

Crack detection is an important measure in the field of structural health monitoring. However, visual crack detection is labor-intensive, time-consuming, inefficient, and expensive. Although image-based detection and processing provides an efficient way for structural crack detection, its accuracy depends on image quality. For engineering structures, especially bridges, the change of light conditions and the difference of surface characteristics of structural components pose a major challenge to traditional crack detection methods. In this paper, a novel crack detection method based on convolutional neural networks is proposed. The development of this method is divided into the following stages. The initial automated crack classification is carried out by using MobileNetV3, and then the improved deepLabv3+ network is used to segment the classified crack image semantically accurately. Finally, the real crack image is used for verification. To verify the proposed method, several conventional deep learning networks are trained and compared. The improved deepLabV3+ integrates MobileNetV3 as its feature extraction backbone and incorporates the convolutional block attention module, which achieves 87.79% average intersection and 93.87% average pixel accuracy on public and real data sets. Compared with traditional models such as VGG16, the proposed method shortens the training time by more than 80% while maintaining high detection accuracy. In addition, the compact parameter configuration and moderate model size make it particularly suitable for deployment on mobile detection devices.

关键词： attention mechanism crack detection improved deepLabv3+ lightweight deep learning semantic segmentation

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