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Journal articles on the topic 'Detection and recognition'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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1

Yekeen, S.A, and T.S Ibiyemi. "EDGE DETECTION ALGORITHM FOR YORUBA CHARACTER RECOGNITION." Advances in Vision Computing: An International Journal (AVC) 5, no. 1/2/3/4 (2018): 01–09. https://doi.org/10.5281/zenodo.3526822.

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Digital image processing for pattern recognition involves several processing and pre-processing steps. Edge detection stands a great position for accurate pattern recognition most especially in character recognition system. Many edge detection techniques were implemented with convolution mask and based on approximations to differential operators. For efficient Yoruba character recognition, compass edge detection algorithm was developed to enhance the recognition rate of Yoruba character. The algorithm developed achieved 0.923 edge detection error rate. The level of accuracy will have been bett

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Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, et al. "Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients." Detection 01, no. 01 (2013): 1–6. http://dx.doi.org/10.4236/detection.2013.11001.

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Shah, Dr Dipti M., and Parul D. Sindha. "Color detection in real time traffic sign detection and recognition system." Indian Journal of Applied Research 3, no. 7 (2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.

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4

Chandiramani, Gunjan, and M. R Dhotre. "Vision Based Road Sign Detection and Recognition." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 43–47. https://doi.org/10.70729/ijser15247.

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Tao, Hongjing, Lei Zhang, Zhipeng Sun, Xinchao Cui, and Weixun Yi. "Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11." Sensors 25, no. 7 (2025): 1983. https://doi.org/10.3390/s25071983.

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The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. These issues stem from the complexities associated with diverse industrial environments and mining conditions, such as the mixing of coal gangue and insufficient illumination within coal mines. A detection model, referred to as EBD-YOLO, is proposed based on YOLOv11n. First, the C3k2-EMA module is integrated with the EMA attention mechanism within the C3k2 module of the backbone network, thereby enhancing the mod

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Hu, Ting. "Anomalous pattern recognition based on image recognition in food safety detection." Journal of Combinatorial Mathematics and Combinatorial Computing 125 (March 30, 2025): 3–20. https://doi.org/10.61091/jcmcc125-01.

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In food processing, foreign matter inevitably contaminates packaged food. To ensure food safety, ray-based detection is used; however, the original images suffer from aberrations and noise that degrade quality and hinder further processing. Thus, images are preprocessed to enhance quality by highlighting key features and suppressing irrelevant ones before abnormal pattern recognition. Following image segmentation, a BP neural network algorithm is applied for foreign object detection. In tests with contaminants such as metal wires, stones, and glass, the algorithm identified distinct abnormal f

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Srilatha, J., T. S. Subashini, and K. Vaidehi. "Solid Waste Detection and Recognition using Faster RCNN." Indian Journal Of Science And Technology 16, no. 42 (2023): 3778–85. http://dx.doi.org/10.17485/ijst/v16i42.2005.

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Shevtekar, Prof Sumit, and Shrinidhi kulkarni. "Traffic-sign Recognition and Detection using Yolo-v8." International Journal of Research Publication and Reviews 5, no. 5 (2024): 1619–31. http://dx.doi.org/10.55248/gengpi.5.0524.1141.

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Yamini, Maidam. "Number Plate Detection in an Image." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25883.

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Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this ar

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M C, Sohan, Akanksh A M, Anala M R, and Hemavathy R. "Banknote Denomination Recognition on Mobile Devices." ECS Transactions 107, no. 1 (2022): 11781–90. http://dx.doi.org/10.1149/10701.11781ecst.

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Several mobile applications have been developed to facilitate denomination detection for blind users. However, none of the existing applications allow for detecting multiple notes in a single frame and relaying the total denomination, nor is there a dataset available for the new Indian currency notes, annotated for object detection training. We describe the development of a detection application that aims to improve on the previously existing solutions by enabling multi-note detection, continuous audio feedback, automatic torch usage, and minimal user-application interaction. YOLOv4 allowed th

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Guo, Boyu, Jianji Wang, Minghui Guo, Miao Chen, Yanan Chen, and Yisheng Miao. "Overview of Pest Detection and Recognition Algorithms." Electronics 13, no. 15 (2024): 3008. http://dx.doi.org/10.3390/electronics13153008.

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Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest det

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Yu, Myoungseok, Narae Kim, Yunho Jung, and Seongjoo Lee. "A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar." Sensors 20, no. 8 (2020): 2321. http://dx.doi.org/10.3390/s20082321.

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In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the co

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Katkar, Aniruddha. "EYE DISEASE RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32078.

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This paper presents an innovative system for detecting eye diseases utilizing advanced machine learning techniques. Given the increasing prevalence of eye disorders, early detection and intervention are of utmost importance. The proposed system integrates a diverse dataset comprising medical images and patient information. Deep learning algorithms are employed to extract intricate features from the dataset. These features are then input into a predictive model, facilitating accurate identification of potential eye diseases. Rigorous testing and validation demonstrate the system's performance a

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C P, Anju, Andria Joy, Haritha Ashok, Joseph Ronald Pious, and Livya George. "Traffic Sign Detection and Recognition." International Journal of Innovative Science and Research Technology 5, no. 7 (2020): 1143–46. http://dx.doi.org/10.38124/ijisrt20jul787.

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As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The propose

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G., Nirmala Priya. "Comparison of Partially Occluded Face Detection and Recognition Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 201–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202099.

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Abid, Muhammad Mobeen, Toqeer Mahmood, Rahan Ashraf, C. M. Nadeem Faisal, Haseeb Ahmad, and Awais Amir Niaz. "Computationally intelligent real-time security surveillance system in the education sector using deep learning." PLOS ONE 19, no. 7 (2024): e0301908. http://dx.doi.org/10.1371/journal.pone.0301908.

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Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques employ template matching and geometric facial features for detecting faces, striving for a balance between detection time and accuracy. To address this issue, the current research presents an enhanced FaceNet network. The RetinaFace is employed to perform expeditious face detection and alignment. Subsequently, FaceNet, with an improved loss function

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Kamble, Kunal, Pranit Jadhav, Atharva Shanware, and Pallavi Chitte. "Smart Surveillance System for Anomaly Recognition." ITM Web of Conferences 44 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224402003.

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Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and author

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Adeyemi, Oladimeji, Martins Irhebhude, and Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques." Advances in Image and Video Processing 7, no. 5 (2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.

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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of f

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Dong, Xuwei, Yang Liu, and Jinpeng Dai. "Recognition of Concrete Surface Cracks Based on Improved TransUNet." Buildings 15, no. 4 (2025): 541. https://doi.org/10.3390/buildings15040541.

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Concrete surface crack detection is a critical problem in the health monitoring and maintenance of engineering structures. The existence and development of cracks may lead to the deterioration of structural performance, potentially causing serious safety accidents. However, detecting cracks accurately remains challenging due to various factors such as uneven lighting, noise interference, and complex backgrounds, which often lead to incomplete or false detections. Traditional manual inspection methods are subjective, inefficient, and costly, while existing deep learning-based approaches still h

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M. P, Milan. "CHALLENGES IN FACE RECOGNITION TECHNIQUE." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 1201–4. http://dx.doi.org/10.51201/jusst/21/07253.

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Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial exp

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21

Chen, Yansong. "Face Mask Recognition based on YOLOV3." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1214–22. http://dx.doi.org/10.54097/cgaz8m04.

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Wearing masks is considered one of the effective ways to prevent epidemics and protect physical health. In densely populated public places, detecting whether the mask is correctly worn has important application value, which can help managers reduce the potential risk of disease transmission. Previous works mainly rely on manual features, which are limited to fully expressing complex scene information. Thanks to the rapid development of artificial intelligence technology, automatic mask detection methods based on deep learning have achieved breakthroughs in speed and accuracy, while mask detect

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22

Achmad, Bimo Vallentino, and Supatman Supatman. "FACIAL PHOTO AUTHENTICITY DETECTION USING FACE RECOGNITION AND LIVENESS DETECTION." Jurnal Teknik Informatika (Jutif) 5, no. 5 (2024): 1423–32. https://doi.org/10.52436/1.jutif.2024.5.5.2328.

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Facial recognition has been widely adopted by many systems as authentication. However, relying on facial photos for authentication is insufficient, as these can be manipulated using printed or digital photos. One method that can be used to prevent this is to integrate face recognition with liveness detection. In this research, face recognition and liveness detection are implemented using a Convolutional Neural Network (CNN) because CNN has the ability to process and extract features from photos effectively. There are two types of datasets used, namely CelebA-Spoof for liveness detection and lf

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G R, Nagarjuna. "Traffic Sign Detection and Recognition." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4581–86. https://doi.org/10.22214/ijraset.2025.69316.

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Traffic sign recognition and detection is a critical component of intelligent transportation systems aimed at enhancing road safety and driver assistance. This technology involves the use of computer vision and machine learning algorithms to detect and classify various road signs such as stop signs, speed limits, and warning signs in real-time. By utilizing cameras or sensors mounted on vehicles, the system identifies these signs and provides timely alerts to the driver. The addition of voice message alerts further improves driver awareness, offering immediate notifications like “Speed limit 5

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Popereshnyak, S. V., R. O. Skoryk, D. V. Kuptsov, and R. V. Kravchenko. "Human face recognition system in video stream." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 296–304. https://doi.org/10.15407/pp2024.02-03.296.

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In the work, an analysis of detection methods and faces in the video stream and their effectiveness in real time was carried out. Modern algorithms and pre-trained models have been found to be able to recognize faces with high accuracy, but their significant drawback is, in particular, vulnerability to attacks using fake faces. Therefore, the work also analyzed approaches to detecting living faces and the possibility of their implementation in the system. Using an object-oriented approach, a tool for face capture, receiving a video stream from various sources, detecting unknown and previously

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S., S., Vibu Krishnan S., Mathan Raj Kumar, Ashok .., and M. Janakiraman. "Object Detection Using Deep Learning." Journal of Cognitive Human-Computer Interaction 6, no. 1 (2023): 32–38. http://dx.doi.org/10.54216/jchci.060103.

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Object recognition is an important task in computer vision that involves identifying the objects such as digital images or videos. This research paper provides a comprehensive review of the different techniques and applications of object recognition. The paper first discusses the basic concepts of object recognition, including feature extraction and matching, classification, and detection. Next, the paper reviews the different techniques for object recognition, such as template matching, PCA-based recognition, and deep learning-based recognition. The paper then presents an overview of the diff

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Poornima Raikar, Pranesh K, and Shreesha A Rao. "Attendance System for College Hostels Using Facial Recognition." International Journal of Current Research and Techniques 15, no. 1 (2025): 50380–84. https://doi.org/10.61359/2024050047.

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Face detection is a computer vision technology designed to identify and locate human faces in digital images. It is a specialized application of object detection, which involves identifying instances of specific semantic objects, such as humans, buildings, or vehicles, in images and videos. With advancements in technology, face detection has become increasingly significant in fields like photography, security, and marketing. This report presents an efficient approach to detecting and recognizing human faces using OpenCV and Python. It explores the pivotal role of machine learning in computer s

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Raut, Prathamesh. "Depression Detection using BDI, Speech Recognition and Facial Recognition." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 347–51. http://dx.doi.org/10.22214/ijraset.2018.4062.

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Anjali, Muneshwar, and Vattam Prof.Jayarajesh. "Face Detection System with Face Recognition." International Organization of Research & Development (IORD) 9, no. 1 (2021): 5. https://doi.org/10.5281/zenodo.5016190.

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The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses the personal characteristics of a person to identify the person's identity. The human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognizes a face as individuals. The stage is then replicated and developed as a model for facial image

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Günlü, Göksel. "Embedded Face Detection and Recognition." International Journal of Advanced Robotic Systems 9, no. 4 (2012): 96. http://dx.doi.org/10.5772/51132.

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Gurnani, Divya, Vijay Gaikwad, and Pradip V. "Intruder Detection and Recognition System." International Journal of Computer Applications 178, no. 20 (2019): 30–34. http://dx.doi.org/10.5120/ijca2019919030.

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Reddy, K. Manideep. "Face Recognition for Criminal Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2856–60. http://dx.doi.org/10.22214/ijraset.2022.44528.

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Abstract: In these days, assessment camera structure wins as a security system at high speed since this structure can screen from remote spots using Web camera joined to video screen by network. Besides, computerized supplies like Web camera, and hard circle drive are proficiently fabricated, and are sold for minimal price. Likewise, execution gain of these mechanized sorts of stuff improves at a fast rate. Current perception camera structure shows dynamic pictures from some oversight areas shot by various Web cameras all the while. Then, this system makes spectator's mind and body tired consi

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Pandey, Amit, Aman Gupta, and Radhey Shyam. "FACIAL EMOTION DETECTION AND RECOGNITION." International Journal of Engineering Applied Sciences and Technology 7, no. 1 (2022): 176–79. http://dx.doi.org/10.33564/ijeast.2022.v07i01.027.

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Facial emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To

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Manira*, Nishika, Swelia Monteiro, Tashya Alberto, Tracy Niasso, and Supriya Patil. "Geo-Landmark Recognition and Detection." Regular issue 10, no. 7 (2021): 95–98. http://dx.doi.org/10.35940/ijitee.g8983.0510721.

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The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural N

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Chobhe, Snehal. "Criminal Detection through Face Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1404–9. http://dx.doi.org/10.22214/ijraset.2021.37067.

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Abstract: Face acknowledge is a champion among the most troublesome subjects in PC vision today. It has applications running from security and perception to delight destinations. Face affirmation writing computer programs are significant in banks, plane terminals, and various associations for screening customers.Germany and Australia have passed on face affirmation at edges and customs for Automatic Passport Control. Human face is a unique dissent having significant degree of change in its appearance which makes stand up to affirmation an irksome issue in PC vision. In this field, exactness an

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Chaudhari, V. J. "Face Recognition and Emotion Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4775–77. http://dx.doi.org/10.22214/ijraset.2021.35698.

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This Face recognition and facial emotion detection is new era of technology. It’s also indirectly defining the level of growth in intelligence, security and copying human emotional behaviour. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural network or wit

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Gorban, Igor I., Vitalij N. Magera, and Sergey V. Levij. "Crime‐detection speaker recognition system." Journal of the Acoustical Society of America 108, no. 5 (2000): 2575. http://dx.doi.org/10.1121/1.4743571.

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STRINGA, LUIGI. "EYES DETECTION FOR FACE RECOGNITION." Applied Artificial Intelligence 7, no. 4 (1993): 365–82. http://dx.doi.org/10.1080/08839519308949995.

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Ye, Li Hua, and Hai Ming Yin. "Video-Tag Detection and Recognition." Applied Mechanics and Materials 58-60 (June 2011): 2528–33. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2528.

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In this work, we present a video-tag detection and recognition method. According to the duration of the video, choose an appropriate strategy to sample the frames. After the candidate tag of every frame is computed, a median filter algorithm is employed to get the tag boundary. At last the binary video-tag is determined by a multi-frame-based analysis algorithm. After scaling the binary tag image to the standard size, a full image-matching algorithm is used to recognize the tag. The experimental results indicate the proposed video-tag detection method has high recall ratio and precision ratio,

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M, Nayan Naik, and Roopa R. "RECOGNITION OF LEAF ILLNESS DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem36642.

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Plant disease early detection has a major impact on crop quality and financial stability, makingit essential for India's agriculture. The precision of current detection techniques is lacking, which puts yields at risk. In order to identify cotton leaf illnesses and classify leaves as healthy, unhealthy, or sick, this study suggests a prediction method. This focused strategy enables focused therapies and aids in the prevention of disease spread. The framework can also be modified to identify diseases that affect tomato plants with the goal of increasing agricultural yields, lowering costs, and

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Li, Zhuan, Junling Wang, Chunmei Liu, and Yonghan Xu. "Bucket detection for object recognition." Journal of Physics: Conference Series 1176 (March 2019): 042032. http://dx.doi.org/10.1088/1742-6596/1176/4/042032.

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Franco, Annalisa, Davide Maltoni, and Serena Papi. "Grocery product detection and recognition." Expert Systems with Applications 81 (September 2017): 163–76. http://dx.doi.org/10.1016/j.eswa.2017.02.050.

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Boutin, Mireille. "Polygon Recognition and Symmetry Detection." Foundations of Computational Mathematics 3, no. 3 (2003): 227–71. http://dx.doi.org/10.1007/s10208-001-0027-5.

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Marcy, Theodore W. "Barotrauma: Detection, Recognition, and Management." Chest 104, no. 2 (1993): 578–84. http://dx.doi.org/10.1378/chest.104.2.578.

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Noor, Shakir Hameed. "Survey Face Detection and Recognition." International Journal of Novel Research in Computer Science and Software Engineering 10, no. 1 (2023): 1–7. https://doi.org/10.5281/zenodo.7534129.

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<strong>Abstract:</strong> In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic

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Mukesh N, Nithis Bangaru B, Vignesh Kumaran A, and Mrs.S.Athilakshmi. "SOS Detection: Distress Signal Recognition." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 782–85. https://doi.org/10.47392/irjaeh.2025.0109.

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In emergency situations where individuals cannot verbally call for help, there is a critical need for a reliable and efficient non-verbal communication system. This project proposes a real-time SOS gesture recognition solution using advanced computer vision techniques to address this need. The system utilizes MediaPipe for precise hand gesture detection and OpenCV for robust image processing to identify a predefined SOS gesture sequence, specifically an open hand followed by a fist. Upon detecting the SOS gesture, the system captures an image of the individual, overlays a timestamp in the bott

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Nishika, Manira, Monteiro Swelia, Alberto Tashya, Niasso Tracy, and Patil Supriya. "Geo-Landmark Recognition and Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 7 (2021): 95–98. https://doi.org/10.35940/ijitee.G8983.0510721.

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The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural N

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Chen, Renxiang, and Xia Tian. "Gesture Detection and Recognition Based on Object Detection in Complex Background." Applied Sciences 13, no. 7 (2023): 4480. http://dx.doi.org/10.3390/app13074480.

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In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interfe

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Zheng, Xibin, Sinan Fang, Haitao Chen, Liang Peng, and Zhi Ye. "Internal Detection of Ground-Penetrating Radar Images Using YOLOX-s with Modified Backbone." Electronics 12, no. 16 (2023): 3520. http://dx.doi.org/10.3390/electronics12163520.

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Geological radar is an important method used for detecting internal defects in tunnels. Automatic interpretation techniques can effectively reduce the subjectivity of manual identification, improve recognition accuracy, and increase detection efficiency. This paper proposes an automatic recognition approach for geological radar images (GPR) based on YOLOX-s, aimed at accurately detecting defects and steel arches in any direction. The method utilizes the YOLOX-s neural network and improves the backbone with Swin Transformer to enhance the recognition capability for small targets in geological r

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Golande, Rushikesh, Rushikesh Bhapkar, Awantika Nalawade, Akash Rashinkar, and Shriganesh Mane. "Weapon Detection System: Real-Time Object Recognition for Threat Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3514–21. https://doi.org/10.22214/ijraset.2025.68105.

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Abstract: The increasing threat to public safety has driven the need for intelligent surveillance systems capable of detecting potential dangers in real time. This study introduces a Weapon Detection System (WDS) that utilizes advanced deep learning and computer vision techniques to identify firearms and other hazardous weapons in public areas. The system employs Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) object detection model to ensure high accuracy and minimal latency in identifying threats from live video streams or images. Designed for real-time deployment, the

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Ma, Yuzhao, Xu Tang, Yaxin Shi, and Pak-Wai Chan. "YOLOv8n–CBAM–EfficientNetV2 Model for Aircraft Wake Recognition." Applied Sciences 14, no. 17 (2024): 7754. http://dx.doi.org/10.3390/app14177754.

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In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Bloc

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