Увійти

Готові списки джерел за темами / Detection feature / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Detection feature.

Статті в журналах з теми "Detection feature"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Detection feature".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.

1

Ling, Xiao, Chuan Zhang, Zhijun Yan, Bo Wang, Qinghong Sheng, and Jun Li. "Infrared Dim and Small Target Detection Based on Local–Global Feature Fusion." Applied Sciences 14, no. 17 (2024): 7878. http://dx.doi.org/10.3390/app14177878.

Повний текст джерела

Анотація:

Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, whi

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Mo, Fuchuan, Shen Liu, Sitong Wu, Ruiyuan Chen, and Tiecheng Song. "YOLO-MFD: Object Detection for Multi-Scenario Fires." Information 16, no. 7 (2025): 620. https://doi.org/10.3390/info16070620.

Повний текст джерела

Анотація:

Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenar

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Zhang, Shijie, Xu Yang, Chao Geng, and Xinyang Li. "A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection." Remote Sensing 16, no. 22 (2024): 4226. http://dx.doi.org/10.3390/rs16224226.

Повний текст джерела

Анотація:

In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adapti

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Deng, Xiangyu, and Shanshan Li. "An Improved SSD Object Detection Algorithm Based on Attention Mechanism and Feature Fusion." Journal of Physics: Conference Series 2450, no. 1 (2023): 012088. http://dx.doi.org/10.1088/1742-6596/2450/1/012088.

Повний текст джерела

Анотація:

Abstract The Single Shot MultiBox Detector (SSD) is a well-known object detection method, but its detection of small objects is not effective. This paper makes modifications to the SSD object detection method to address its insufficient semantic information in low-level feature maps, thus enhancing the detectability for small objects. First, the Feature Pyramid Network (FPN) is incorporated into the SSD so that the shallow feature map, which is primarily utilized for detecting small objects, contains more semantic information in addition to rich location information. Second, the Convolutional

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Liu, Gang, Jiangtao Xi, Jun Tong, and Hongpeng Xu. "An Infrared Aircraft Detection Algorithm Based on Context Perception Feature Enhancement." Electronics 13, no. 14 (2024): 2695. http://dx.doi.org/10.3390/electronics13142695.

Повний текст джерела

Анотація:

To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two featu

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Jin, Ruixia, Yihao Wang, Yuanyuan Ma, Tao Li, and Xintao Duan. "Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature." Symmetry 13, no. 10 (2021): 1775. http://dx.doi.org/10.3390/sym13101775.

Повний текст джерела

Анотація:

Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which prov

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Khalid, Noor Aldeen A., Muhammad Imran Ahmad, Thulfiqar H. Mandeel, and Mohd Nazrin Md Isa. "Palmprint features matching based on KAZE feature detection." Journal of Physics: Conference Series 1878, no. 1 (2021): 012055. http://dx.doi.org/10.1088/1742-6596/1878/1/012055.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Fu, Xinchuan, Rui Yu, Weinan Zhang, Li Feng, and Shihai Shao. "Pedestrian Detection by Feature Selected Self-Similarity Features." IEEE Access 6 (2018): 14223–37. http://dx.doi.org/10.1109/access.2018.2803160.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Zhang, Yifan, Haiyan Kang, and Qiang Wang. "MMFDetect: Webshell Evasion Detect Method Based on Multimodal Feature Fusion." Electronics 14, no. 3 (2025): 416. https://doi.org/10.3390/electronics14030416.

Повний текст джерела

Анотація:

In the context of escalating network adversarial challenges, effectively identifying a Webshell processed using evasion techniques such as encoding, obfuscation, and nesting remains a critical challenge in the field of cybersecurity. To address the poor detection performance of the existing Webshell detection methods for evasion samples, this study proposes a multimodal feature fusion-based evasion Webshell detection method (MMF-Detect). This method extracts RGB image features and textual vector features from two modalities: the visual and semantic modalities of Webshell file content. A multim

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Zhang, Zhiqiang, Xin Qiu, and Yongzhou Li. "Learning Balance Feature for Object Detection." Electronics 11, no. 17 (2022): 2765. http://dx.doi.org/10.3390/electronics11172765.

Повний текст джерела

Анотація:

In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and the detection head to enable shallow features with more semantic information. However, FPN is insufficient for object detection on various scales, especially for small-scale object detection. One of the reasons is that the features are extracted at different network depths, which introduces gaps between features.

Стилі APA, Harvard, Vancouver, ISO та ін.

11

Hofer-Schmitz, Katharina, Ulrike Kleb, and Branka Stojanović. "The Influences of Feature Sets on the Detection of Advanced Persistent Threats." Electronics 10, no. 6 (2021): 704. http://dx.doi.org/10.3390/electronics10060704.

Повний текст джерела

Анотація:

This paper investigates the influences of different statistical network traffic feature sets on detecting advanced persistent threats. The selection of suitable features for detecting targeted cyber attacks is crucial to achieving high performance and to address limited computational and storage costs. The evaluation was performed on a semi-synthetic dataset, which combined the CICIDS2017 dataset and the Contagio malware dataset. The CICIDS2017 dataset is a benchmark dataset in the intrusion detection field and the Contagio malware dataset contains real advanced persistent threat (APT) attack

Стилі APA, Harvard, Vancouver, ISO та ін.

12

Li, Hongli, Zhiqi Yi, Liye Mei, et al. "SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion." Processes 12, no. 5 (2024): 931. http://dx.doi.org/10.3390/pr12050931.

Повний текст джерела

Анотація:

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightw

Стилі APA, Harvard, Vancouver, ISO та ін.

13

Yang, Yanni, and Shengnan Lu. "Vehicle Detection in Videos Leveraging Multi-Scale Feature and Memory Information." Electronics 14, no. 10 (2025): 2009. https://doi.org/10.3390/electronics14102009.

Повний текст джерела

Анотація:

Vehicle detection in videos is a critical task in traffic monitoring. Existing vehicle detection tasks commonly use static detectors. Since video frames are processed as discrete static images, static detectors neglect the temporal information of vehicles when detecting vehicles in videos, leading to a reduction in detection accuracy. To address the above shortcoming, this paper improves the detection performance by introducing a video vehicle detection method that combines multi-scale features with memory information. We design a Multi-scale Feature Generation Network (MFGN) to improve the de

Стилі APA, Harvard, Vancouver, ISO та ін.

14

Xie, Fei, Jianguo Ju, Tongtong Zhang, et al. "A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid." Sensors 23, no. 24 (2023): 9723. http://dx.doi.org/10.3390/s23249723.

Повний текст джерела

Анотація:

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the

Стилі APA, Harvard, Vancouver, ISO та ін.

15

Fei, Ke, Qi Li, Can Cui, et al. "Nontechnical Loss Detection using Neural Architecture Search and Outlier Detection." E3S Web of Conferences 256 (2021): 01025. http://dx.doi.org/10.1051/e3sconf/202125601025.

Повний текст джерела

Анотація:

Electricity supply is essential to economy growth and improvement of people’s life. For a long time, illegal electricity theft not only affects the supply of power, but also causes significant economic loss. Traditional techniques for detecting electricity theft are inefficient and time-consuming. Data-based detecting algorithms become a new solution. This article analyses the features of electricity consumption, current, voltage and opening records under various electricity theft modes and proposes a new simulation method for electricity theft users. Based on the simulation dataset, a feature

Стилі APA, Harvard, Vancouver, ISO та ін.

16

Chen, Haolin, Qi Wang, Weijian Ruan, et al. "ALFPN: Adaptive Learning Feature Pyramid Network for Small Object Detection." International Journal of Intelligent Systems 2023 (April 21, 2023): 1–14. http://dx.doi.org/10.1155/2023/6266209.

Повний текст джерела

Анотація:

Object detection has become a crucial technology in intelligent vision systems, enabling automatic detection of target objects. While most detectors perform well on open datasets, they often struggle with small-scale objects. This is due to the traditional top-down feature fusion methods that weaken the semantic and location information of small objects, leading to poor classification performance. To address this issue, we propose a novel feature pyramid network, the adaptive learnable feature pyramid network (ALFPN). Our approach features an adaptive feature inspection that incorporates learn

Стилі APA, Harvard, Vancouver, ISO та ін.

17

Liu, Shuai, Ying Xu, Lingming Guo, Meng Shao, Guodong Yue, and Dong An. "Multi-scale personnel deep feature detection algorithm based on Extended-YOLOv3." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 773–86. http://dx.doi.org/10.3233/jifs-200778.

Повний текст джерела

Анотація:

Tens of thousands of work-related injuries and deaths are reported in the construction industry each year, and a high percentage of them are due to construction workers not wearing safety equipment. In order to address this safety issue, it is particularly necessary to automatically identify people and detect the safety characteristics of personnel at the same time in the prefabricated building. Therefore, this paper proposes a depth feature detection algorithm based on the Extended-YOLOv3 model. On the basis of the YOLOv3 network, a security feature recognition network and a feature transmiss

Стилі APA, Harvard, Vancouver, ISO та ін.

18

Zhao, Xiaofeng, Wenwen Zhang, Hui Zhang, Chao Zheng, Junyi Ma, and Zhili Zhang. "ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles." Drones 8, no. 4 (2024): 161. http://dx.doi.org/10.3390/drones8040161.

Повний текст джерела

Анотація:

A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of model missed and false detections caused by complex ground background and uneven target scale in UAV aerial infrared image target detection, as well as high computational complexity. Firstly, an improved YOLOv8 backbone feature extraction network is designed based on the lightweight network GhostHGNetV2. It can effectively capture target feature information at different scales, improving target detection accuracy in complex environments while remaining lightweight. Secondly, the VoVGSCSP impro

Стилі APA, Harvard, Vancouver, ISO та ін.

19

Li, Nianfeng, Zhenyan Wang, Yongyuan Huang, Jia Tian, Xinyuan Li, and Zhiguo Xiao. "A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion." Sensors 24, no. 12 (2024): 3758. http://dx.doi.org/10.3390/s24123758.

Повний текст джерела

Анотація:

Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion. This method combines global and local attention through an improved attention feature fus

Стилі APA, Harvard, Vancouver, ISO та ін.

20

Wang, Jinkang, Xiaohui He, Faming Shao, et al. "A Novel Attention-Based Lightweight Network for Multiscale Object Detection in Underwater Images." Journal of Sensors 2022 (September 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/2582687.

Повний текст джерела

Анотація:

Underwater images have low quality, and underwater targets have different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from underwater images. In this study, a lightweight underwater multiscale target detection model with an attention mechanism is designed to solve the above problems. In this model, MobileNetv3 is used as the backbone network for preliminary feature extraction. The lightweight feature extraction module (LFEM) pays attention to the feature map at the channel and space levels. The features with large weights are promoted, while

Стилі APA, Harvard, Vancouver, ISO та ін.

21

Li, Kangning, Peigang Jiao, Jiaming Ding, and Weibo DU. "Bearing defect detection based on the improved YOLOv5 algorithm." PLOS ONE 19, no. 10 (2024): e0310007. http://dx.doi.org/10.1371/journal.pone.0310007.

Повний текст джерела

Анотація:

In the field of bearing defect detection, Aiming at the problem of low efficiency in manual inspection and prone to missed detections in scenarios with small target defects, and overlapping targets, an improved YOLOv5-based object detection method is proposed. Firstly, in terms of feature extraction, the C3 modules in the original backbone of YOLOv5 are replaced with the finer-grained Res2Block modules to improve the model’s feature extraction ability. Secondly, in terms of feature fusion, a Bidirectional Feature Pyramid Network (BiFPN) is added to the original neck of YOLOv5 to enhance the fu

Стилі APA, Harvard, Vancouver, ISO та ін.

22

Li, Qiming, and Hongwei Shi. "YOLO-GE: An Attention Fusion Enhanced Underwater Object Detection Algorithm." Journal of Marine Science and Engineering 12, no. 10 (2024): 1885. http://dx.doi.org/10.3390/jmse12101885.

Повний текст джерела

Анотація:

Underwater object detection is a challenging task with profound implications for fields such as aquaculture, marine ecological protection, and maritime rescue operations. The presence of numerous small aquatic organisms in the underwater environment often leads to issues of missed detections and false positives. Additionally, factors such as the water quality result in weak target features, which adversely affect the extraction of target feature information. Furthermore, the lack of illumination underwater causes image blur and low contrast, thereby increasing the difficulty of the detection t

Стилі APA, Harvard, Vancouver, ISO та ін.

23

Lee, Sang, Dong Kim, and Jong Park. "Cost-Sensitive Spam Detection Using Parameters Optimization and Feature Selection." JUCS - Journal of Universal Computer Science 17, no. (6) (2011): 944–60. https://doi.org/10.3217/jucs-017-06-0944.

Повний текст джерела

Анотація:

E-mail spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients' system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning techniques have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should counteract with it. To cope with this, parameters optimization and feature selection have been used to reduce processing overheads while guaranteeing high detection rates. However, previous approa

Стилі APA, Harvard, Vancouver, ISO та ін.

24

Lin, Haitao, Xiangru Li, and Ziying Luo. "Pulsars detection by machine learning with very few features." Monthly Notices of the Royal Astronomical Society 493, no. 2 (2020): 1842–54. http://dx.doi.org/10.1093/mnras/staa218.

Повний текст джерела

Анотація:

ABSTRACT It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features,

Стилі APA, Harvard, Vancouver, ISO та ін.

25

Zhang, Changfan, Xinliang Hu, Jing He, and Na Hou. "Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion." Journal of Advanced Transportation 2022 (March 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/1172654.

Повний текст джерела

Анотація:

The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. First, multiscale feature maps are obtained from a feature extraction backbone network. The proposed multiscale feature fusion network then fuses the underlying features of the original three scales. These fused features contain more defect semantic information and location details. Based on the fused features, a path aggregation network is used

Стилі APA, Harvard, Vancouver, ISO та ін.

26

Yousefi-Azar, Mahmood, Len Hamey, Vijay Varadharajan, and Shiping Chen. "Byte2vec: Malware Representation and Feature Selection for Android." Computer Journal 63, no. 8 (2019): 1125–38. http://dx.doi.org/10.1093/comjnl/bxz121.

Повний текст джерела

Анотація:

Abstract Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2v

Стилі APA, Harvard, Vancouver, ISO та ін.

27

Yang, Sijie, Xiaohua Wang, and Wenjie Wang. "Recognize highly similar sewing gestures by the robot." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502210772. http://dx.doi.org/10.1177/15589250221077267.

Повний текст джерела

Анотація:

The autonomous and efficient learning of sewing gestures by robots will bring great convenience to the garment industry. To improve the accuracy of robots in detecting sewing gestures with high similarity, three detection models based on deep learning are proposed in the paper. First, in order to improve the detection accuracy and detection speed of sewing gestures under complex backgrounds, we added a dense connection layer to the low-resolution network layer of YOLO-V3 to enhance the transmission and reuse rate of image features. Secondly, a deeper ResNet50 residual network is introduced to

Стилі APA, Harvard, Vancouver, ISO та ін.

28

Qu, Shenming, Chaoxu Dang, Wangyou Chen, and Yanhong Liu. "SMA-YOLO: An Improved YOLOv8 Algorithm Based on Parameter-Free Attention Mechanism and Multi-Scale Feature Fusion for Small Object Detection in UAV Images." Remote Sensing 17, no. 14 (2025): 2421. https://doi.org/10.3390/rs17142421.

Повний текст джерела

Анотація:

With special consideration for complex scenes and densely distributed small objects, this frequently leads to serious false and missed detections for unmanned aerial vehicle (UAV) images in small object detection scenarios. Consequently, we propose a UAV image small object detection algorithm, termed SMA-YOLO. Firstly, a parameter-free simple slicing convolution (SSC) module is integrated in the backbone network to slice the feature maps and enhance the features so as to effectively retain the features of small objects. Subsequently, to enhance the information exchange between upper and lower

Стилі APA, Harvard, Vancouver, ISO та ін.

29

Zhang, Ruifang, Bohan Deng, Xiaohui Cheng, and Hong Zhao. "GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection." Sensors 24, no. 21 (2024): 6781. http://dx.doi.org/10.3390/s24216781.

Повний текст джерела

Анотація:

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model’s paramet

Стилі APA, Harvard, Vancouver, ISO та ін.

30

Revathy, S., and S. Sathya Priya. "Enhancing the Efficiency of Attack Detection System Using Feature selection and Feature Discretization Methods." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (2023): 156–60. http://dx.doi.org/10.17762/ijritcc.v11i4s.6322.

Повний текст джерела

Анотація:

Intrusion detection technologies have grown in popularity in recent years using machine learning. The variety of new security attacks are increasing, necessitating the development of effective and intelligent countermeasures. The existing intrusion detection system (IDS) uses Signature or Anomaly based detection systems with machine learning algorithms to detect malicious activities. The Signature-based detection rely only on signatures that have been pre-programmed into the systems, detect known attacks and cannot detect any new or unusual activity. The Anomaly based detection using supervise

Стилі APA, Harvard, Vancouver, ISO та ін.

31

Luo, Xin, Jiatian Li, Xiaohui A, and Yuxi Deng. "A Combined Frame Difference and Convolution Method for Moving Vehicle Detection in Satellite Videos." Sensors 25, no. 2 (2025): 306. https://doi.org/10.3390/s25020306.

Повний текст джерела

Анотація:

To address the challenges of missed detections caused by insufficient shape and texture features and blurred boundaries in existing detection methods, this paper introduces a novel moving vehicle detection approach for satellite videos. The proposed method leverages frame difference and convolution to effectively integrate spatiotemporal information. First, a frame difference module (FDM) is designed, combining frame difference and convolution. This module extracts motion features between adjacent frames using frame difference, refines them through backpropagation in the neural network, and in

Стилі APA, Harvard, Vancouver, ISO та ін.

32

Ma, Xiaoxiao, Xinai Lu, Yihong Huang, et al. "An Advanced Chicken Face Detection Network Based on GAN and MAE." Animals 12, no. 21 (2022): 3055. http://dx.doi.org/10.3390/ani12213055.

Повний текст джерела

Анотація:

Achieving high-accuracy chicken face detection is a significant breakthrough for smart poultry agriculture in large-scale farming and precision management. However, the current dataset of chicken faces based on accurate data is scarce, detection models possess low accuracy and slow speed, and the related detection algorithm is ineffective for small object detection. To tackle these problems, an object detection network based on GAN-MAE (generative adversarial network-masked autoencoders) data augmentation is proposed in this paper for detecting chickens of different ages. First, the images wer

Стилі APA, Harvard, Vancouver, ISO та ін.

33

Roslan, Shairatul Akma, Muhamad Sharifuddin Abd Rahim, Fitri Yakub, Yong Chee Kong, and Norzailawati Mohd Noor. "Artificial Intelligence for rapid mapping of potential archaeological features using Bag of Visual Words based image classifier." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012030. https://doi.org/10.1088/1755-1315/1412/1/012030.

Повний текст джерела

Анотація:

Abstract Integrating Artificial Intelligence technological advancements in archaeology has revolutionised automated feature detection, presenting a novel perspective on archaeological feature recognition and image interpretation. This approach reduces costs associated with ground data collection and enhances the reliability and productivity of large-scale archaeological mapping. Consequently, this study aims to explore feature detection and matching techniques in archaeological detection using Artificial Intelligence and Scale-Invariant Feature Transform and Oriented Fast and Rotated Brief alg

Стилі APA, Harvard, Vancouver, ISO та ін.

34

Prajakta, H. Umale Chanchal H. Sahani Aboli S. Patil Anisha A. Gedam Kajal V. Kawale Prof. Aditya Turankar. "Planer Object Detection Using Sift and Surf in Image Processing." International Journal of Research in Computer & Information Technology 7, no. 2 (2022): 31–34. https://doi.org/10.5281/zenodo.6676111.

Повний текст джерела

Анотація:

Object Detection refers to the capability of computers and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique that works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) and propose various geometric transformations. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points

Стилі APA, Harvard, Vancouver, ISO та ін.

35

Passmore, Greg. "Proximity Modulated Thresholding for Hessian Matrix Feature Detection." Journal of Mathematical Techniques and Computational Mathematics 04, no. 01 (2025): 01–06. https://doi.org/10.33140/jmtcm.04.01.05.

Повний текст джерела

Анотація:

Our lab processes large volumes of multispectral drone data. We use feature identification for image alignment, object recognition, and scene reconstruction. Traditional methods using the Hessian matrix detect features like corners or blobs. This method is commonly used for color images, and the issues with changes in lighting and exposure are well known. However, for our multispectral data, it proved especially problematic. Fixed thresholds worsened these issues, causing inefficiencies and inaccuracies in feature matching and image alignment. This paper presents a dynamic thresholding approac

Стилі APA, Harvard, Vancouver, ISO та ін.

36

Nikhat Fatima and Dr. Sameena Banu. "Deep Fake Detection with A Unified Discrepancy-Aware Forgery Detection Network and Attention-Guided Feature Rectification." International Research Journal on Advanced Science Hub 7, no. 02 (2025): 60–69. https://doi.org/10.47392/irjash.2025.008.

Повний текст джерела

Анотація:

Deepfake technology, which allows the creation of manipulated, yet highly realistic, content have made it difficult to ascertain the integrity of any form of digital media. In order to solve this problem, we introduce an end-to-end deep-learned framework called Discrepancy-Aware Forgery Detection Network (DAFDN) dedicated to the task of detecting forged media to tackle representation biases and capture irregular patterns in forgery samples. This consists of a Feature Representation Extractor (FRE) and a Feature Refinement Module (FRM), and both jointly generates representative but not biased f

Стилі APA, Harvard, Vancouver, ISO та ін.

37

Özer, Erman, Murat İskefiyeli, and Jahongir Azimjonov. "Toward lightweight intrusion detection systems using the optimal and efficient feature pairs of the Bot-IoT 2018 dataset." International Journal of Distributed Sensor Networks 17, no. 10 (2021): 155014772110522. http://dx.doi.org/10.1177/15501477211052202.

Повний текст джерела

Анотація:

Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain

Стилі APA, Harvard, Vancouver, ISO та ін.

38

Guan, Zekai. "A Review of Feature Matching Based Image Tampering Detection Methods." Applied and Computational Engineering 135, no. 1 (2025): 49–56. https://doi.org/10.54254/2755-2721/2025.21080.

Повний текст джерела

Анотація:

In recent years, digital image tampering detection techniques play an increasingly important role in dealing with digital image authenticity detection. This review analyzes in detail the feature matching based image tampering detection methods that mainly target the copy-paste tampering problem. Among such methods are subdivided into two subcategories: feature block-based and feature point-based detection. Traditional methods such as feature-point based SIFT and feature-block based Zernike Moments match by extracting local features or global features, but their performance is limited under hig

Стилі APA, Harvard, Vancouver, ISO та ін.

39

Abdullakutty, Faseela, Pamela Johnston, and Eyad Elyan. "Fusion Methods for Face Presentation Attack Detection." Sensors 22, no. 14 (2022): 5196. http://dx.doi.org/10.3390/s22145196.

Повний текст джерела

Анотація:

Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal perform

Стилі APA, Harvard, Vancouver, ISO та ін.

40

Kharwar, Ankit Rajeshkumar, and Devendra V. Thakor. "An Ensemble Approach for Feature Selection and Classification in Intrusion Detection Using Extra-Tree Algorithm." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijisp.2022010113.

Повний текст джерела

Анотація:

The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to f

Стилі APA, Harvard, Vancouver, ISO та ін.

41

Oyeniyi, Samuel A., and Joseph A. Ojeniyi. "DEVELOPMENT OF A CONCEPTUAL FRAMEWORK AND A MEASUREMENT MODEL FOR THE DETECTION OF FAKE NEWS." International Journal of Innovative Research in Advanced Engineering 8, no. 7 (2021): 138–47. http://dx.doi.org/10.26562/ijirae.2021.v0807.001.

Повний текст джерела

Анотація:

Fake news has been there since before the advent of the Internet. It has had an immense impact on our modern society. Detecting fake news is an important step. Although there are various ways and methods in which fake news can be detected and solved. In this research paper we discuss the various conceptual frameworks and how they affect fake news. It further shows the development of the conceptual framework and the measurement model used; showing which of the frameworks fake news is most likely to surface through. The objective of the research is to design a conceptual framework for fake news

Стилі APA, Harvard, Vancouver, ISO та ін.

42

Ye, Chao, Jiangang Ding, Huaizhi Wang, Ming Yang, Aojia Tian, and Wei Li. "Enhanced Security Contraband Detection through Integration of Attention Mechanism in R3Det." Journal of Physics: Conference Series 2589, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2589/1/012002.

Повний текст джерела

Анотація:

Abstract Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced

Стилі APA, Harvard, Vancouver, ISO та ін.

43

Cooper, G. R. J., and D. R. Cowan. "Feature Detection Using Sunshading." ASEG Extended Abstracts 2003, no. 2 (2003): 1–4. http://dx.doi.org/10.1071/aseg2003ab029.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

44

Moe, Moe Htay. "Feature extraction and classification methods of facial expression: a survey." Computer Science and Information Technologies 2, no. 1 (2020): 26–32. https://doi.org/10.11591/csit.v2i1.p26-32.

Повний текст джерела

Анотація:

Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial expression recognition system (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recogn

Стилі APA, Harvard, Vancouver, ISO та ін.

45

Hui, Jiapeng, Guoyun Lian, Jiansheng Wu, Shuting Ge, and Jinfeng Yang. "Proportional feature pyramid network based on weight fusion for lane detection." PeerJ Computer Science 10 (January 29, 2024): e1824. http://dx.doi.org/10.7717/peerj-cs.1824.

Повний текст джерела

Анотація:

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This article proposes a Proportional Feature Pyramid Network (P-FPN) through fusing the weights into the FPN for lane detection. For obtaining a more accurately dete

Стилі APA, Harvard, Vancouver, ISO та ін.

46

Li, Shuaicai, Xiaodong Yang, Xiaoxia Lin, Yanyi Zhang, and Jiahui Wu. "Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5." Sensors 23, no. 12 (2023): 5634. http://dx.doi.org/10.3390/s23125634.

Повний текст джерела

Анотація:

Aerial vehicle detection has significant applications in aerial surveillance and traffic control. The pictures captured by the UAV are characterized by many tiny objects and vehicles obscuring each other, significantly increasing the detection challenge. In the research of detecting vehicles in aerial images, there is a widespread problem of missed and false detections. Therefore, we customize a model based on YOLOv5 to be more suitable for detecting vehicles in aerial images. Firstly, we add one additional prediction head to detect smaller-scale objects. Furthermore, to keep the original feat

Стилі APA, Harvard, Vancouver, ISO та ін.

47

Wu, Yutian, Shuming Tang, Shuwei Zhang, and Harutoshi Ogai. "An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving." Applied Sciences 9, no. 20 (2019): 4363. http://dx.doi.org/10.3390/app9204363.

Повний текст джерела

Анотація:

Feature Pyramid Network (FPN) builds a high-level semantic feature pyramid and detects objects of different scales in corresponding pyramid levels. Usually, features within the same pyramid levels have the same weight for subsequent object detection, which ignores the feature requirements of different scale objects. As we know, for most detection networks, it is hard to detect small objects and occluded objects because there is little information to exploit. To solve the above problems, we propose an Enhanced Feature Pyramid Object Detection Network (EFPN), which innovatively constructs an enh

Стилі APA, Harvard, Vancouver, ISO та ін.

48

Gharaee, Hossein, and Maryam Fekri. "A new feature selection for intrusion detection system." International Journal of Academic Research 7, no. 4 (2015): 48–60. http://dx.doi.org/10.7813/2075-4124.2015/7-4/a.8.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

49

Feng, Lu, Zhuangzhi Wu, and Xiang Long. "Fast Image Diffusion for Feature Detection and Description." International Journal of Computer Theory and Engineering 8, no. 1 (2016): 58–62. http://dx.doi.org/10.7763/ijcte.2016.v8.1020.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

50

Zhao, Yongsheng, Yuxing Gao, Xu Yang, and Luyang Yang. "Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities." Applied Sciences 15, no. 11 (2025): 5857. https://doi.org/10.3390/app15115857.

Повний текст джерела

Анотація:

Multispectral detection leverages visible and infrared imaging to improve detection performance in complex environments. However, conventional convolution-based fusion methods predominantly rely on local feature interactions, limiting their capacity to fully exploit cross-modal information and making them more susceptible to interference from complex backgrounds. To overcome these challenges, the YOLO-MEDet multispectral target detection model is proposed. Firstly, the YOLOv5 architecture is redesigned into a two-stream backbone network, incorporating a midway fusion strategy to integrate mult

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!