共查询到20条相似文献，搜索用时 15 毫秒
1.
The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain. 相似文献
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R. H. Aswathy P. Suresh Mohamed Yacin Sikkandar S. Abdel-Khalek Hesham Alhumyani Rashid A. Saeed Romany F. Mansour 《计算机、材料和连续体（英文）》2022,70(2):2097-2111
In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%. 相似文献
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目的 针对人工分拣组成的零件包装盒常常会出现缺少部分零件的问题,开发一套集训练、识别、分选于一体的智能分拣系统.方法 在设计过程中,提出一种基于深度学习的改进Yolov3算法,针对工业现场光照、业零件形状和质地等实际因素,对Yolo算法的训练和检测进行改进,通过对包装盒产品的一次拍摄,检测出画面中出现的预设物体,并与标准设置相比对,从而判断出该盒内产品是否有缺料、多料的情况,以此分选出合格与否的包装盒.结果 在物体摆放相互重叠不超过20％的情况下,物体检测的准确率为98.2％,召回率为99.5％.结论 通过文中提出的改进算法,设计的检测系统能够在复杂的工业现场环境下正常工作,并能对包装的完整性进行准确的检测. 相似文献
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The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning. 相似文献
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磁性纳米材料的制备及其在军事上的应用 总被引：2，自引：0，他引：2
磁性纳米材料因其独特的性质,在军事中有着广泛的应用,在武器装备的发展中占有举足轻重的地位。本综述介绍磁性纳米材料的制备方法,如机械球磨法、化学沉淀法、磁控溅射法等,归纳各种方法的优缺点;阐述磁性纳米材料在军事上的应用,并展望其研究前景。 相似文献
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In recent years, the number of exposed vulnerabilities has grown rapidly andmore and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect the malicious payload and identify their categories with high accuracy, we proposed a packet-based malicious payload detection and identification algorithm based on object detection deep learning network. A dataset of malicious payload on code execution vulnerability has been constructed under the Metasploit framework and used to evaluate the performance of the proposed malware detection and identification algorithm. The experimental results demonstrated that the proposed object detection network can efficiently find and identify malicious payloads with high accuracy. 相似文献
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煤炭港在使用装船机的溜筒卸载煤的过程中会产生扬尘,港口为了除尘,需要先对粉尘进行检测。为解决粉尘检测问题,本文提出一种基于深度学习(YOLOv4-tiny)的溜筒卸料煤粉尘的检测方法。利用改进的YOLOv4-tiny算法对溜筒卸料粉尘数据集进行训练和测试,由于检测算法无法获知粉尘浓度,本文将粉尘分为四类分别进行检测,最后统计四类粉尘的检测框总面积,通过对这些数据做加权和计算近似判断粉尘浓度大小。实验结果表明,四类粉尘的检测精度(AP)分别为93.98%、93.57%、80.03%和57.43%,平均检测精度(mAP)为81.27%,接近YOLOv4的83.38%,而检测速度(FPS)为25.1,高于YOLOv4的13.4。该算法较好地平衡了粉尘检测的速率和精度,可用于实时的粉尘检测以提高抑制溜筒卸料产生的煤粉尘的效率。 相似文献
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Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification. 相似文献
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This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) method in the image frequency domain. The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks. In order to improve the training efficiency, images are first transformed into the frequency domain during a preprocessing phase. The algorithm is then calibrated using the flattened frequency data. LSTM is used to improve the performance of the developed network for long sequence data. The accuracy of the developed model is 99.05%, 98.9%, and 99.25%, respectively, for training, validation, and testing data. An implementation framework is further developed for future application of the trained model for large-scale images. The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time. The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection. 相似文献
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目的 将基于深度学习的YOLOv5算法应用于PCB裸板的缺陷检测上,以提高检测的准确率。方法 通过增加特征融合通路,将C2、C3、C4层直接与P2、P3、P4层相连,从而减小信息的损耗;引入更浅层的C2、F2、P2特征图以增加图像的细节信息;并且使用注意力机制SE_block,大幅提高原算法的准确率。结果 改进后的网络的平均精度由91.54%提高至97.36%,提高了5.82%,并且对于各类缺陷,算法的检测精度都能保持在90%以上,满足工业的需求。结论 文中的算法提高了检测精度,体现了浅层信息在小目标检测上的作用,验证了多信息融合通路的优势,彰显了注意力机制的优越性,相比于原算法具有一定的优势。 相似文献
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目的 解决现有工业线束导线排序检测方法中存在的效率低、混色导线检测效果差等问题。方法 基于机器视觉技术设计一种线束导线排序检测装置,并结合图像处理技术和深度学习原理提出一种混色导线排序检测方法。首先根据线束图像中选择的感兴趣区域,分割出线束连接器图像和导线图像,并采用模板匹配和颜色定位方法完成连接器正反面的识别和单色导线的识别定位;然后采集并制作PE混色导线数据集,研究Faster R−CNN、SSD、YOLOv3和YOLOv5m等4种不同目标检测算法对PE混色导线的检测效果。结果 实验结果表明,YOLOv5m检测模型的检测速度和准确率兼顾性最好;改进系统后,检测时间减少了18.55%,平均识别准确率为98.83%。结论 改进后检测系统具有良好的检测效率和可靠性,适用于种类丰富的工业线束导线排序检测。 相似文献
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Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that detect driver actions and help them drive safely has increased. In these studies, although some distinct data types, such as the physical conditions of the driver, audio and visual features, and vehicle information, are used, the primary data source is images of the driver that include the face, arms, and hands taken with a camera inside the car. In this study, an architecture based on a convolution neural network (CNN) is proposed to classify and detect driver distraction. An efficient CNN with high accuracy is implemented, and to implement intense convolutional networks for large-scale image recognition, a new architecture was proposed based on the available Visual Geometry Group (VGG-16) architecture. The proposed architecture was evaluated using the StateFarm dataset for driver-distraction detection. This dataset is publicly available on Kaggle and is frequently used for this type of research. The proposed architecture achieved 96.95% accuracy. 相似文献
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为了解决复杂场景下激光跟踪仪对合作目标靶球的精确识别难题,提出了基于深度学习的合作目标靶球高效检测方法。首先分析了合作目标靶球的图像特征,然后采用改进的YOLOv2模型,针对合作目标靶球多尺度与小目标占比多的特点,提出了一种基于注意力机制的改进方法,同时为提高网络模型对复杂背景的抗干扰能力,提出了一种数据增强方法。测试结果表明,所提出的基于注意力机制与数据增强的改进YOLOv2模型对复杂背景的抗干扰能力较强,且对合作目标靶球的检测精度有显著提高,在合作目标靶球测试集上的检测准确率达到92.25%,能够有效满足激光跟踪仪在大型装置精密装配过程中的目标检测精度需求。 相似文献
14.
Ning Ding;Xu Bao;Shantong Sun;Yun Wang; 《International journal of imaging systems and technology》2024,34(2):e22999
Urinary sediment image detection, as one of the three major routine clinical tests in medical practice, is an important method for physical examination and diagnosis of urinary system diseases. Crystalluria detection is a subtask of urinary sediment image detection, focusing on detecting and identifying crystalline components in urine. To address the issues of low accuracy and inefficiency caused by small crystal granularity in crystalluria detection, we propose the Dilated Bilinear Space Pyramid ConvNext Network (DBSPC-Net), which achieves high-precision real-time crystalluria detection. DBSPC-Net ingeniously combines dilated convolution pooling with bilinear space pyramid, introducing Dilated Bilinear Space Pyramid Pooling (DBSPP) to enlarge the receptive field and capture information at multiple scales. Additionally, we utilize the Normalized Gaussian Wasserstein Distance Loss (NWDLoss) instead of Intersection over Union (IoU) to enhance the recognition of small targets. Finally, the ConvNext module is employed to fuse local and global features, enhancing urine crystal recognition accuracy and speed. The crystalluria dataset is sourced from 400 actual patients in a hospital. It comprises five main types of urine crystals, namely calcium oxalate dihydrate, calcium oxalate monohydrate, uric acid, ammonium magnesium phosphate, and cystine. Experimental results demonstrate that the proposed improved model achieves an average precision of 87.34% and a detection time of 7.9 ms per urine crystal image. DBSPC-Net can accurately and rapidly identify crystalluria objects in scenarios involving microscope mica compensation, meeting the requirements of algorithmic detection accuracy and real-time performance in crystalluria detection. 相似文献
15.
《工程（英文）》2020,6(3):291-301
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder. 相似文献
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There are numerous internet-connected devices attached to the industrial process through recent communication technologies, which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things (IIoTs). Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services, external trusted authorities, and centralized architectures; they have high computation and communication costs, low performance, and are exposed to a single authority of failure and bottleneck. Blockchain technology (BC) is widely adopted in the industrial sector for its valuable features in terms of decentralization, security, and scalability. In our work, we propose a decentralized, scalable, lightweight, trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application. The proposed scheme offers a double layer of data encryption, device authentication, scalability, high performance, low power consumption, and improves the industry’s operations; provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage. We also address data aggregation operations, which are considered challenging in electric power smart grids. We utilize a multi-chain proof of rapid authentication (McPoRA) as a consensus mechanism, which helps to enhance the computational performance and effectively improve the latency. The advanced reduced instruction set computer (RISC) machines ARM Cortex-M33 microcontroller adopted in our work, is characterized by ultra-low power consumption and high performance, as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm (ECDSA). This improves the computational execution, increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer. Our experimental results show that the proposed scheme achieves excellent performance, data security, real-time data processing, low power consumption (70.880 mW), and very low memory utilization (2.03% read-only memory (RAM) and 0.9% flash memory) and execution time (0.7424 s) for the cryptographic algorithm. This enables autonomous network reconfiguration on-demand and real-time data processing. 相似文献
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Naglaa F. Soliman Abeer D. Algarni Walid El-Shafai Fathi E. Abd El-Samie Ghada M. El Banby 《计算机、材料和连续体（英文）》2021,69(2):1571-1595
Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things (IoT) networks. The objective of using cancelable biometrics is to save the original ones from hacking attempts. A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications. The original biometric is blurred with two co-prime operators. Hence, it can be recovered as the Greatest Common Divisor (GCD) between its two blurred versions. Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation. Hence, the ability to change cancelable templates is guaranteed, since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image. Furthermore, we test the utility of the proposed algorithm in the single- and multi-biometric scenarios. The multi-biometric scenario depends on compressing face, fingerprint, iris, and palm print images, simultaneously, to generate the cancelable templates. Evaluation metrics such as Equal Error Rate (EER) and Area and Receiver Operator Characteristic curve (AROC) are considered. Simulation results on single- and multi-biometric scenarios show high AROC values up to 99.59%, and low EER values down to 0.04%. 相似文献
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Currently, the Internet of Things (IoT) is revolutionizing communication technology by facilitating the sharing of information between different physical devices connected to a network. To improve control, customization, flexibility, and reduce network maintenance costs, a new Software-Defined Network (SDN) technology must be used in this infrastructure. Despite the various advantages of combining SDN and IoT, this environment is more vulnerable to various attacks due to the centralization of control. Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service (DDoS) attacks, but they often lack mechanisms to mitigate their severity. This paper proposes a Multi-Attack Intrusion Detection System (MAIDS) for Software-Defined IoT Networks (SDN-IoT). The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms. First, a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets: the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) and the Canadian Institute for Cybersecurity Intrusion Detection Systems (CICIDS2017), to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems. The algorithms evaluated include Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Second, an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems (IDS) was developed to enable effective comparison between the datasets used in the development of the security scheme. The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system, with average accuracies of 99.88% and 99.89%, respectively. Furthermore, the proposed security scheme reduced the false alarm rate by 33.23%, which is a significant improvement over prevalent schemes. Finally, tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset, making it the best for IDS compared to the NSL-KDD dataset. 相似文献