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An adaptive and lightweight YOLOv5 detection model for lung tumor in PET/CT images

SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Zhou, Tao Ye, Xinyu Lu, Huiling Guo, Yujie Wang, Hongxia Liu, Yang North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Med Informat & Engn Yinchuan 750004 Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Yinchuan 750021 Peoples R China

Multi-modal medical images are important in tumor lesion detection. However, the existing detection models only use single-modal to detect lesions, a multi-modal semantic correlation is not enough to consider and lacks ability to express the shape, size, and contrast degree features of lesions. A Cross Modal YOLOv5 model (CMYOLOv5) is proposed. Firstly, there are two networks, auxiliary network is consisted by dual-branch structure to extract semantic information from PET and CT, backbone network is consisted by YOLOv5 to extract semantic information from PET/CT. Secondly, Cross-modal Features Fusion (CFF) is designed in auxiliary network to fuse PET functional information and CT anatomical information. Self-Adaptive Attention Fusion (AAF) is designed in backbone network to fuse and enhance three-modal complementary information. Thirdly, Self-Adaptive Transformer (SAT) is designed in feature enhance neck. Using Transformer with deformable attention mechanism to focus on lung tumor region. Using MLP with channel attention mechanism to enhance features representation ability of lung tumor region. Finally, Reparameter Residual Block (RRB) and Reparameter Convolution operation (RC) are designed to fully learn richer PET, CT and PET/CT feature. Comparative experiments are conducted on clinical lung tumor PET/CT multi-modality dataset, the effectiveness of CMYOLOv5 is verified by Precision, Recall, mAP, F1, FPS, and training time, experimental results are 97.16%, 96.41%, 97.18%, 96.78%, 96.37 and 3912 s. CMYOLOv5 has high precision in the detection of irregular lung tumors, which is superior to the existing advanced methods.

关键词： Cross-modal fusion Self-adaptive transformer PET/CT Lung tumor detection

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A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral image Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Ou, Xianfeng Liu, Liangzhen Tu, Bing Zhang, Guoyun Xu, Zhi Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Images & Graph Intelligent Proc Guilin 541004 Peoples R China

Change detection approaches can detect changed areas of the same scene at different times. Hyperspectral remote-sensing images contain large amounts of spectral information at high resolution. As hyperspectral datasets become abundant, more and more change detection technologies use hyperspectral images as raw data. Hyperspectral images suffer from band redundancy. There is an urgent need to improve the directionality of change of information features. To solve these problems, in this article, we propose a CNN framework involving slow-fast band selection (SFBS) and feature fusion grouping (SFBS-FFGNET) for hyperspectral image change detection. The main contributions of this article are as follows: 1) based on slow feature analysis (SFA), an SFBS method is proposed, which selects slow and fast feature bands to better extract changed and unchanged features, to more effectively separate changed and unchanged pixels;2) we used a difference matrix to enrich the level of change information to provide more change characteristics for change detection;and 3) an FFG method was used to generate a more discriminative feature group, and the related loss function was designed. Experimental results on multiple real hyperspectral datasets showed that SFBS can reduce the operating load on the computer and improve the accuracy of change detection, and FFG can also improve the accuracy of change detection. In summary, SFBS-FFGNET is superior to most existing change detection methods.

关键词： Feature extraction Hyperspectral imaging Deep learning Data mining Convolutional neural networks Change detection algorithms Tensors Change detection difference matrix feature fusion grouping (FFG) hyperspectral image slow-fast band selection (SFBS)

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MADNet: A Fast and Lightweight Network for Single-image Super Resolution

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第3期51卷 1443-1453页

作者： Lan, Rushi Sun, Long Liu, Zhenbing Lu, Huimin Pang, Cheng Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka 8048550 Japan Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the single-image super-resolution (SISR) task with great improvement in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). However, most of the existing CNN-based SR models require high computing power, which considerably limits their real-world applications. In addition, most CNN-based methods rarely explore the intermediate features that are helpful for final image recovery. To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning. Specifically, a residual multiscale module with an attention mechanism (RMAM) is developed to enhance the informative multiscale feature representation ability. Furthermore, we present a dual residual-path block (DRPB) that utilizes the hierarchical features from original low-resolution images. To take advantage of the multilevel features, dense connections are employed among blocks. The comparative results demonstrate the superior performance of our MADNet model while employing considerably fewer multiadds and parameters.

关键词： Feature extraction Computational modeling image reconstruction image resolution Task analysis Computer architecture Cybernetics Channel attention dense connections image super resolution lightweight multiscale mechanism

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Panoptic Feature Pyramid Network Applications In intelligent Traffic 16

Panoptic Feature Pyramid Network Applications In Intelligent...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Lu, Chang Lei, Xiaochun Xie, Junlin Wang, Xiaolong Mu, Xiangboge Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

ISBN: (纸本)9781665404457

intelligenta transportation is an important part of urban development. The core of realizing intelligent transportation is to master the urban road condition. This system processes the video of dashcam based on the Panoptic Segmentation network and adds a tracking module based on the comparison of front and rear frames and KM algorithm. The system mainly includes the following parts: embedded device, Panoptic Feature Pyramid Network, cloud server and Web site.

关键词： intelligent transportation Panoptic Segmentation traffic forecast styling

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CCGL-YOLOV5:A cross-modal cross-scale global-local attention YOLOV5 lung tumor detection model*

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期165卷 107387-107387页

作者： Zhou, Tao Liu, Fengzhen Ye, Xinyu Wang, Hongwei Lu, Huiling North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Med Informat & Engn Yinchuan 750004 Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Yinchuan 750021 Peoples R China

Background: Multimodal medical image detection is a key technology in medical image analysis, which plays an important role in tumor diagnosis. There are different sizes lesions and different shapes lesions in multimodal lung tumor images, which makes it difficult to effectively extract key features of lung tumor ***: A Cross-modal Cross-scale Clobal-Local Attention YOLOV5 Lung Tumor Detection Model (CCGLYOLOV5) is proposed in this paper. The main works are as follows: Firstly, the Cross-Modal Fusion Transformer Module (CMFTM) is designed to improve the multimodal key lesion feature extraction ability and fusion ability through the interactive assisted fusion of multimodal features;Secondly, the Global-Local Feature Interaction Module (GLFIM) is proposed to enhance the interaction ability between multimodal global features and multi modal local features through bidirectional interactive branches. Thirdly, the Cross-Scale Attention Fusion Module (CSAFM) is designed to obtain rich multi-scale features through grouping multi-scale attention for feature ***: The comparison experiments with advanced networks are done. The Acc, Rec, mAP, F1 score and FPS of CCGL-YOLOV5 model on multimodal lung tumor PET/CT dataset are 97.83%, 97.39%, 96.67%, 97.61% and 98.59, respectively;The experimental results show that the performance of CCGL-YOLOV5 model in this paper are better than other typical ***: The CCGL-YOLOV5 model can effectively use the multimodal feature information. There are important implications for multimodal medical image research and clinical disease diagnosis in CCGL-YOLOV5 model.

关键词： YOLOV5 Cross-modal attention fusion Global-local feature fusion Cross-scale attention fusion PET/CT tumor image

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Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022年第5期16卷 926-938页

作者： Yang, Chao Wang, Xiaoping Chen, Zhanfei Zhang, Sen Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Classical conditioning (CC) and operant conditioning (OC), also known as associative memory, are two of the most fundamental and critical learning mechanisms in the biological brain. However, the existing designs of associative memory memristive circuits mainly focus on CC, and few studies have used memristors to imitate OC at the behavioral level, as well as the OC-CC cascaded associative memories that are widespread in biological learning processes. This work proposes an OC-CC cascaded circuit composed of OC and CC circuits. With the OC memristive circuit, bio-like functions such as random exploration, feedback learning, experience memory, and experience-based decision-making are achieved, which enables the circuit to continuously reshape its own memories and actions to adapt to changing environments. By cascading it with the CC memristive circuit that has the functions of associative learning, forgetting, generalization, and differentiation, the OC-CC cascaded circuit implements richer associative memories and has stronger environmental adaptability. Finally, the proposed circuits can perform on-line in-situ learning and in-memory computing. This is a more brain-like processing method, which is different from the von Neumann architecture. The simulation results of the proposed circuits in PSPICE show that they can simulate the above functions and have advantages in power consumption and hardware overhead. This work provides a possible realization idea for large-scale bionic learning.

关键词： Memristors Associative memory Mice Electric shock Behavioral sciences Integrated circuit modeling In-memory computing Associative memory bionic learning classical conditioning in-memory computing memristor operant conditioning

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Application of the EfficientDet Algorithm in Traffic Flow Statistics 16

Application of the EfficientDet Algorithm in Traffic Flow St...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Lin, Kaihao Chen, Junyan Chen, Aoge Huang, Hu Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

ISBN: (纸本)9781665404457

With the continuous development of artificial intelligence technology, deep learning technology is used to process a large number of real-time traffic scene information helping the management of public transportation, and traffic flow statistics can reflect the real-time traffic conditions. The paper uses the EfficientDet target detection algorithm to detect and analyze the traffic video frame information and carry out statistics of vehicle and pedestrian flow at traffic *** system can calculate the vehicle speed and perceive the degree of traffic congestion in real-time. It's convenient for the traffic department to increase the utilization rate of the road.

关键词： EfficientDet Algorithm Traffic Flow Statistics Vehicle Speed Calculating Transportation Planning

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Meter Recognition And Reading System Based On Machine Car 16

Meter Recognition And Reading System Based On Machine Car

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Wang Xiaolong Chen Junyan Tang Yongqing Gao Jie Wang Xingbo Zhan Pufan Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

ISBN: (纸本)9781665404457

In the field of automatic inspection and factory quality inspection, it is often necessary to analyze the readings of many different types of meters to understand the working status of the factory. Therefore, it is particularly important to get an accurate reading. To solve this problem, an automatic meter reading system based on core technologies such as image recognition, deep learning, internet of things and intelligent machine car was designed. We adopted YOLOV3, SIFT, FLANN, Hough transform and other efficient algorithms to achieve the function of reading meter. Our system is competent for the reading of digital meter as well as pointer meter. Experiments show that our system has a good effect.

关键词： Automatic meter reading Automatic inspection Deep learning Internet of things intelligent machine car

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Semi-supervised Classification of Data Streams Based on Adaptive Density Peak Clustering 27th

Semi-supervised Classification of Data Streams Based on Adap...

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27th International Conference on Neural Information processing

作者： Liu, Changjie Wen, Yimin Xue, Yun Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Hunan City Univ Sch Municipal & Surveying Engn Yiyang Peoples R China

ISBN: (纸本)9783030638320;9783030638337

In the real-world scenario of data stream classification, label scarcity is very common. More challenges are data streams always include concept drifts. To handle these challenges, an algorithm of semi-supervised classification of data streams based on adaptive density peak clustering (SSCADP) is proposed. In SSCADP, to generate concept clusters at leaves in a Hoeffding tree, a density peak clustering method and a change detection technique are combined to adaptively locate the clustering centers. Concerning concept drift detection, we argue that the change of cluster with higher density more likely reflect the change of data distribution. Hence, to detect concept drifts, an adaptive weighting method for density change detection is proposed to calculate the deviations between the history concept clusters and new ones. Experiments on synthetic and real datasets confirm the advantages of SSCADP.

关键词： Semi-supervised classification Data stream Decision tree Clustering Concept drift

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Prescribed Performance Control of Two-Time-Scale Systems and Its Application

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年

作者： Zeng, Ze-Hong Wang, Yan-Wu Liu, Xiao-Kang Yang, Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Guangxi Univ Sch Elect Engn Nanning Peoples R China

This article explores the prescribed performance control design for linear two-time-scale systems (TTSSs). Due to ill-conditioning and high dimensionality, existing prescribed performance control methods for single-time-scale systems are unsuitable for TTSSs. Moreover, current TTSS methodologies focus on steady-state performance, often neglecting transient dynamics. To address these challenges, we first apply the Chang transformation to decouple the fast and slow states. Next, we use a state transformation to convert the state equation into block form to eliminate the requirement of a full row-rank input matrix. Finally, the backstepping method is utilized to design the prescribed performance control. The effectiveness and advantages of the proposed control strategy are demonstrated through two examples: a numerical simulation and a hardware-in-the-loop experiment involving an electronic circuit system.

关键词： backstepping control prescribed performance control two-time-scale system underactuated system

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