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real-time detection and location of reserved anchor hole in coal mine roadway support steel belt

JOURNAL OF real-time image processing 2023年第5期20卷 89页

作者： Wang, Hongwei Zhang, Fujing Wang, Haoran Li, Zhenglong Wang, Yuheng Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Safety & Emergency Management Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Min Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Shanxi Engn Res Ctr Coal Mine Intelligent Equipmen Taiyuan 030024 Peoples R China Shanxi Coking Coal Grp Co Ltd Postdoctoral Workstat Taiyuan 030024 Peoples R China

Based on the current coal mine roadway using supporting steel belt steel belt auxiliary anchor support, the location of the supporting steel belt anchor holes is mainly done manually;if the location is not accurate, there will be large safety hazards and other problems. An intelligent real-time detection and location method of anchor holes in coal mine roadway support steel belt based on deep learning model and depth camera is proposed. First, to reduce the influence of water mist and dust on the camera and improve the image quality of the camera, the image is pre-processed using contrast limited adaptive histogram equalization. Second, the YOLOv5s model is improved by adding SPD-Conv and coordinate attention mechanisms to improve the detection capability of the model. Third, a real-time depth map restoration method that fully preserves object edge features is proposed to avoid errors caused by areas with depth values of 0 in the depth map when locating anchor holes in combination with a depth camera. Finally, the improved YOLOv5s model proposed in this paper combined with the repaired depth map was used to achieve the detection and location of anchor holes in a laboratory simulated tunnel with a location error of less than 5 mm and an average FPS of 28.9. In summary, the real-time target detection and location method based on a deep learning model and a depth camera is feasible in an unstructured environment in coal mines.

关键词： deep learning real-time detection Detection and location of anchor hole Improved YOLOv5 network Coordinate attention mechanism

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Toward User-Guided Seismic Facies Interpretation With a Pre-Trained Large Vision Model

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IEEE ACCESS 2025年 13卷 42965-42976页

作者： Atolagbe, Joshua Koeshidayatullah, Ardiansyah King Fahd Univ Petr & Minerals Dept Geosci Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Integrat Petr Res Dhahran 31261 Saudi Arabia

Recent advancements in computer vision and deep learning have made a paradigm shift in seismic facies interpretation. Various supervised and unsupervised deep learning-based seismic facies research using Convolutional Neural Network (CNN) as the baseline architecture have been proposed and achieved promising results. However, these approaches are often limited by the lack of available high-quality labelled dataset and minimum explainability of the model. This is further compounded by the fact that these models do not allow user-guided interaction, which limits the ability of seismic interpreters to perform localized seismic facies segmentation through prompts. To optimize the already existing approach, we present a time-efficient and high precision alternative by reformulating seismic facies segmentation as a Segment All or Segment One (SASO) task. In this paper, we propose FaciesSAM, based on Fast Segment Anything Model, for seismic facies segmentation. For the first time, our paper highlights that FaciesSAM can help to decouple seismic facies identification into all-instance segmentation (segment all) and prompt-guided selection (segment one) for broad and localized facies interpretation, respectively. The effectiveness of our proposed method was evaluated on the benchmark F3 geological dataset. Experimental results shows that our method is effective, even when trained on fewer seismic images, achieving a mAP0.5 of 83.3% with 3.1% increase in pixel accuracy and 4.6% increase in mean class accuracy when compared to the CNN benchmark results. Furthermore, our method meets real-time processing speed of 1.14ms per seismic section. These results underscore the capacity of prompt-based CNN detectors to solving specific geological challenges such as seismic interpretation accuracy and processing speed.

关键词： image segmentation Convolutional neural networks Computational modeling Training Computer vision Transformers Petroleum Head Feature extraction Data models Facies segmentation prompt-based FaciesSAM deep learning model explainability

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Research on automatic classification and detection of chicken parts based on deep learning algorithm

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JOURNAL OF FOOD SCIENCE 2023年第10期88卷 4180-4193页

作者： Chen, Yan Peng, Xianhui Cai, Lu Jiao, Ming Fu, Dandan Xu, Chen Chen Zhang, Peng Wuhan Polytech Univ Sch Mech Engn Wuhan Peoples R China

Accurate classification and identification of chicken parts are critical to improve the productivity and processing speed in poultry processing plants. However, the overlapping of chicken parts has an impact on the effectiveness of the identification process. To solve this issue, this study proposed a real-time classification and detection method for chicken parts, utilizing YOLOV4 deep learning. The method can identify segmented chicken parts on the assembly line in real time and accurately, thus improving the efficiency of poultry processing. First, 600 images containing multiple chicken part samples were collected to build a chicken part dataset after using the image broadening technique, and then the dataset was divided according to the 6:2:2 division principle, with 1200 images as the training set, 400 images as the test set, and 400 images as the validation set. Second, we utilized the single-stage target detector YOLO to predict and calculate the chicken part images, obtaining the categories and positions of the chicken leg, chicken wing, and chicken breast in the image. This allowed us to achieve real-time classification and detection of chicken parts. This approach enabled real-time and efficient classification and detection of chicken parts. Finally, the mean average precision (mAP) and the processing time per image were utilized as key metrics to evaluate the effectiveness of the model. In addition, four other target detection algorithms were introduced for comparison with YOLOV4-CSPDarknet53 in this study, which include YOLOV3-Darknet53, YOLOV3-MobileNetv3, SSD-MobileNetv3, and SSD-VGG16. A comprehensive comparison test was conducted to assess the classification and detection performance of these models for chicken parts. Finally, for the chicken part dataset, the mAP of the YOLOV4-CSPDarknet53 model was 98.86% on a single image with an inference speed of 22.2 ms, which was higher than the other four models of YOLOV3-Darknet53, YOLOV3-MobileNetv3, SSD-Mo

关键词： chicken parts comparison test deep learning detection effect target detection

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SP-CrackNet: serial-parallel network with boundary contrastive learning for real-time crack detection

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SIGNAL image AND VIDEO processing 2024年第4期18卷 3265-3274页

作者： Xie, Ying Yin, Hui Chong, Aixin Yang, Ying Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Beijing Railway Engn Beijing 100044 Peoples R China

Crack detectors based on deep learning have made tremendous progress compared to inefficient traditional inspections. However, missing thin local structures, blurred boundary segmentation and slow inference speed restrict the performance improvement of existing crack detectors. To this end, we propose a serial-parallel network (SP-CrackNet) for pixel-level real-time crack detection. Specifically, a serial-parallel feature extractor with global bottleneck blocks (GB Block) is proposed. Based on the serial-parallel structure, SPFE can enlarge the receiving field and capture the local information of thin cracks effectively, and the GB Block designed by us can ensure the continuity of the slender cracks is sensed with a lower computational cost. Moreover, a boundary contrastive learning scheme (BCL scheme) is designed to enhance the learning ability of SP-CrackNet for crack boundary features, thereby improving the segmentation accuracy of crack boundary regions. Extensive experiments on CFD and deepCrack datasets show that SP-CrackNet outperforms the comparative methods while achieving real-time inference speed.

关键词： real-time crack detection Serial-parallel structure Boundary contrastive learning Global bottleneck block

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An Adaptive learning Rate deep learning Optimizer Using Long and Short-Term Gradients Based on G-L Fractional-Order Derivative

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NEURAL processing LETTERS 2024年第2期56卷 106-106页

作者： Chen, Shuang Zhang, Changlun Mu, Haibing Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Commun & Informat Syst Beijing 100044 Peoples R China

deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grunwald-Letnikov (G-L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G-L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.

关键词： deep learning Optimization algorithm Adaptive learning rate Fractional-order derivative

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Multi-view image Fusion Using Ensemble deep learning Algorithm For MRI And CT images

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2024年第3期23卷 1-24页

作者： Thenmoezhi, N. Perumal, B. Lakshmi, A. Kalasalingam Acad Res & Educ Dept Elect & Commun Engn Sch Elect Elect & Biomed Technol Krishnankoil 626126 Tamil Nadu India Ramco Inst Technol Dept Elect & Commun Engn Rajapalayam 626117 Tamil Nadu India

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble deep learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing-based k-Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)

关键词： Multi-view image fusions Magnetic Resonance Imaging (MRI) images Computed Tomography (CT) images Enhanced Lion Swarm Optimization (ELSO) Ensemble deep learning (EDL) algorithm

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Development of an Intelligent Weld Surface Defect Detection System: Design and Application

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第3期28卷 12-21页

作者： Wang, Guan-Qiang Zeng, He Chen, Ming-Song Lin, Yong-Cheng Zhang, Chi-Zhou Tan, Xian-Hua Guilin Univ Elect Sci & Technol Sch Mech & Elect Engn Guilin Peoples R China Natl Univ Def Technol Sch Intelligent Sci Changsha Peoples R China Cent South Univ Changsha Peoples R China Cent South Univ Light Alloy Res Inst Changsha Peoples R China Cent South Univ Sch Mech & Elect Engn Changsha Peoples R China Cent South Univ SLAM Deep Learning & Multisensory Fus Technol Nav Changsha Peoples R China

Early evaluation through the detection of surface defects in welds is essential to ensure the quality of weldments. In this study, considering the complexity of weld surface inspection, we designed a high-performance inspection system that combines image processing, defect detection, and model evolution functions. This system facilitates the engineering application of image super-resolution reconstruction, defect detection, and semi-supervised learning methods in real-world weld detection scenarios through the integrated weld surface inspection module and a visually interactive client. The experiment results demonstrate that the system can perform real-time processing, with a speed surpassing 30 frames per second, for super-resolution and detection evaluation of input images. Furthermore, it has autonomous learning capabilities, which continuously improve detection precision and reduce the miss rate during long-time detection.

关键词： Surface reconstruction Welding Superresolution Inspection Semisupervised learning real-time systems Surface treatment System analysis and design image reconstruction Defect detection Defect detection

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Lightweight lane line detection based on learnable cluster segmentation with self-attention mechanism

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第3期17卷 518-529页

作者： Yang, Qin Ma, Yahong Li, Linsen Su, Chang Gao, Yujie Tao, Jiaxin Huang, Zhentao Jiang, Rui Xijing Univ Sch Informat Engn Xian Peoples R China Autocore Ai Nanjing Peoples R China

Pixel segmentation is one of the most commonly used deep learning methods for modern lane line detection. Although deep segmentation outperforms traditional methods, there are two main problems: slow speed and limited receptive field. In response to these problems, this paper proposes a lightweight lane line detection algorithm based on learnable cluster segmentation and self-attention mechanism, which has extremely fast speed and the ability to adapt to real scenes. The lane detection process is considered as clustering under row segmentation. The data is processed through row segmentation and fed into a self-attention mechanism. In addition to the benchmark dataset for lane detection, the algorithm was ported to real vehicles for real-time road testing. Two tests show that our method performs very well on TuSimple, with an accuracy of 97.15%, an F1 score of 73.5 on CULane, and a speed of 142.7 frames per second (FPS), which solves the problem of slow cluster segmentation, while improving the accuracy of row segmentation. In the new scenario, the method has a misjudgment rate of only 6.7% for lane line points, which is suitable for the high standard requirements of autonomous driving.

关键词： traffic engineering computing image segmentation feature extraction slow cluster segmentation self-attention mechanism pattern clustering row segmentation extremely fast speed learning (artificial intelligence) lane line points lane detection process modern lane line detection Neural nets pixel segmentation Optical, image and video signal processing Computer vision and image processing techniques commonly used deep learning methods deep segmentation convolutional neural nets limited receptive field Traffic engineering computing object detection lightweight lane line detection algorithm deep learning (artificial intelligence) image recognition slow speed road vehicles learnable cluster segmentation

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FIRESTART: Fire Ignition Recognition with Enhanced Smoothing Techniques and real-time Tracking 22nd

FIRESTART: Fire Ignition Recognition with Enhanced Smoothing...

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22nd International Conference on image Analysis and processing (ICIAP)

作者： Zedda, Luca Loddo, Andrea Di Ruberto, Cecilia Univ Cagliari Dept Math & Comp Sci Cagliari Italy

ISBN: (纸本)9783031510229;9783031510236

Fires can potentially cause significant harm to both people and the environment. Recently, there has been a growing interest in real-time fire and smoke detection to provide practical assistance. Detecting fires in outdoor areas is crucial to safeguard human lives and the environment. This is especially important in situations where more than traditional smoke detectors may be required. In this work, we propose FIRESTART, which aims to achieve accurate and robust ignition detection for prompt identification and response to fire incidents. The proposed framework utilizes a lightweight deep learning architecture and post-processing techniques for fire-starting interval detection. Its evaluation was conducted on the ONFIRE dataset, comparing it with several state-of-the-art methods. The results are encouraging, particularly from computational and real-time use perspectives.

关键词： deep learning Computer Vision image processing Vision Transformers Fire Detection

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Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

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WIRELESS NETWORKS 2024年第5期30卷 4543-4554页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci N-5063 Bergen Norway Brandon Univ Dept Math & Comp Sci Brandon MB Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan

Consumer demand for automobiles is changing because of the vehicle's dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet's frame rate from 71.2 to 89.17 and VGG19's frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

关键词： Vehicle classification deep learning Fine-grained classification Vehicular traffic Sensor data

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