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Traffic Lights detection and Recognition with New Benchmark Datasets Using Deep Learning and tensorflow object detection api

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TRAITEMENT DU SIGNAL 2022年第5期39卷 1673-1683页

作者： Kilic, Irfan Aydin, Galip Firat Univ Dept Comp Engn TR-23119 Elazig Turkey

Today, traffic lights are widely used in places with high vehicle traffic. Especially in autonomous vehicles, fast and high accuracy detection and recognition of traffic lights are critical. Machine learning methods are generally used to do this. Deep learning models give more successful results than machine learning methods in detecting the exact location of traffic lights in different climatic conditions. In this study, Faster R-CNN Inception v2 deep learning model was trained and tested on two different datasets that we prepared and published publicly under variable traffic and climatic conditions in Turkey. Successful results were obtained with fewer data by using the Transfer Learning method with the help of tensorflow object detection api in the training of the model. It has been shown that the datasets we have prepared can be developed considering the conditions in other countries and successful results will be obtained.

关键词： traffic lights deep learning benchmark datasets tensorflow object detection api object detection and recognition Faster RCNN

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Evaluating computer vision approaches for counting exposed aggregate number on pavement surface

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KSCE JOURNAL OF CIVIL ENGINEERING 2025年第5期29卷

作者： Chhay, Lyhour Kim, Jaehoon Lee, Seung Woo Minist Publ Work & Transports Publ Infrastruct Dept St 598Sangkat Chrang Chamres 2 Phnom Penh 12000 Cambodia Korea Inst Civil Engn & Bldg Technol Korea Construct Stand Ctr 283 Goyang Daero Goyang 10223 Gyeonggi Do South Africa Gangneung Wonju Natl Univ Dept Civil Engn Jeebeon Gil 7 Gangneung Si 25457 Gangwon Do South Korea

Two mainstream solutions for counting the expose aggregate number (EAN) on expose aggregate concrete pavement (EACP) surface are evaluated in this paper. The EAN represents the average wavelength of pavement texture attributed to its correlation. This parameter affects the tire-pavement noise. The EAN is estimated manually by human counting that requires a considerable amount of effort and is time consuming. Recently, computer-vision technologies have accomplished notable success in the counting task. Several state-of-the-art technologies for object counting are proposed for achieving different targets. Therefore, the capability of current states-of-the-art technologies are evaluated to identify if they can be performed for EAN counting tasks because of the complexity characteristic of aggregates. Two deep learning models used for evaluating the EAN counting are Faster-RCNN and LC-FCN. The EACP surface image dataset is constructed for the implemented models. The tensorflow-Library and Pytorch-Framework are used to fine-tune parameters in the Faster-RCNN and LC-FCN model, respectively. The result indicates that both models achieve a similar accuracy of approximately 70%. The LC-FCN achieves a lower mean absolute error. Further, both methods are preliminarily acceptable for counting the aggregate with their limitation and under a given condition which aggregate is not often occluded and distinguishable between the background and object.

关键词： Expose aggregate concrete pavement Aggregate counting tensorflow object detection api Blob detection LC-FCN

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Counting of exposed aggregate number on pavement surface based on computer vision technique

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ROAD MATERIALS AND PAVEMENT DESIGN 2023年第1期24卷 86-102页

作者： Chhay, Lyhour Park, Lae-Jeong Kim, Young Kyu Lee, Seung Woo Gangneung Wonju Natl Univ Dept Civil Engn Gangneung Si South Korea Gangneung Wonju Natl Univ Dept Elect Engn Gangneung Si South Korea Gangneung Wonju Natl Univ Inst Disaster Prevent Gangneung Si South Korea

Functional performance of exposed aggregate concrete pavement (EACP), such as tyre-pavement noise, is influenced by the surface texture depth and wavelength. To minimise tyre-pavement noise, the texture wavelength, which is represented by the exposed aggregate number (EAN), should be controlled. Normally, the EAN is conducted by manual counting. It requires much human efforts. Therefore, this study suggests an efficient method to count the EAN on a digital image of EACP surface through the deep learning model faster region-based convolutional neural network (Faster R-CNN). The tensorflow object detection api was used to adjust the parameters in the training model. Results of the suggested model were compared with the manual counting in the mock-up and field test dataset. The result showed that the mean absolute error was 5.34 and 8.19 for the mock-up and field tests, respectively. Therefore, the proposed method can be used to preliminarily estimate the EAN under specified condition.

关键词： Exposed aggregate concrete pavement exposed aggregate counting object detection and counting computer vision tensorflow object detection api

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VIRTUAL YOGA TEACHER WITH AI MODEL FOR YOGA POSTURE CORRECTION FOR GOOD HEALTH 15

VIRTUAL YOGA TEACHER WITH AI MODEL FOR YOGA POSTURE CORRECTI...

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15th ITU Kaleidoscope Conference : Innovation and Digital Transformation for a Sustainable World

作者： Yenishetti, Swapna Karajkhede, Ganesh Panat, Lakshmi Ctr Dev Adv Comp Bengaluru Karnataka India

ISBN: (纸本)9798350367799;9789261390914

With the advent of preventive and promotive health in the world, driven by the WHO's Sustainable Development Goal for Healthcare to ensure healthy lives and promote wellbeing for all ages it is a necessity to develop Artificial Intelligence driven tools for Yoga. Yoga is the art and science of healthy living and has a history spanning thousands of years. Hence there is genuine need of AI model for Yoga posture correction, to reap the benefits of practice of Yoga. Developing AI models for yoga posture correction has the potential to enhance the experience for yoga followers and acting as a Virtual Yoga teacher. In this study, we are focusing on identification of 10 important yoga poses performed by the user with the help of Pre trained Move Net model and transfer learning. Using such deep learning algorithms, an individual can understand the gap between current and ideal way of performing the specific yoga asana, thus enabling correction in the yoga pose. This process is to be completed in real-time and needs to be interactive necessitating the usage of *** library. The system analyzes the difference between the actual and ideal yoga pose and landmarks the image of human body while performing the yoga pose with required correction.

关键词： MoveNet Transfer learning Yoga Posture detection tensorflow object detection api Deep Neural Network

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Sign Language detection Using Machine Learning 2nd

Sign Language Detection Using Machine Learning

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2nd International Semantic Intelligence Conference (ISIC)

作者： Ilanchezhian, P. Singh, I. Amit Kumar Balaji, M. Kumar, A. Manoj Yaseen, S. Muhamad Sona Coll Technol IT Dept Salem Tamil Nadu India

ISBN: (纸本)9789811971280;9789811971266;9789811971259

Sign language detection project is to detect the sign language hand gestures, which really helps the common people like is to understand what a deaf or mute people are trying to converse with us. The sign language detection translates the sign language, in which user forms a hand shape that is structured signs or gestures. In sign language, the configuration of the fingers, the orientation of the hand, and the relative position of fingers and hands to the body are the expressions of a deaf and mute person. Based on this application, the user must be able to capture images of the hand signs or gestures using web camera and they shall predict the hand signs or meaning of the sign and display the name of sign language on screen. At first, we will be taking sample images of different signs, for example, hello, eat, thankyou, etc. Then we are going to label the images with the LabelImg python application file, which is very helpful for object detection. The LabelImg application file develops an XML document for the corresponding image for the training process. In the training process, we have used tensorflow object detection api to train our model. After training the model, we have detected the sign language or hand gestures in real time;with the help of OpenCV-python, we access the webcam and load the configs and trained model, so that we have detected the sign languages in real time.

关键词： Sign language Hand gestures LabelImg tensorflow object detection api

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object detection for Autonomous Vehicle Using tensorflow

Object Detection for Autonomous Vehicle Using TensorFlow

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Conference on Intelligent Computing, Information and Control Systems (ICICCS)

作者： Howal, Sadanand Jadhav, Aishwarya Arthshi, Chandrakirti Nalavade, Sapana Shinde, Sonam RIT Islampur India

ISBN: (纸本)9783030304652;9783030304645

The area of computer vision is emerging continually with the increasing interaction and development to provide a comfortable interaction between human and machines. One of the key aspects in the process of computer vision is object detection. Either objects can be identified partially or close to the original objects. The accuracy in detecting the objects can be improved by using state-of-the-art deep learning models like faster-Regional Convoluted Neural Network (faster-RCNN), You Only Look Once model (YOLO), Single Shot Detector (SSD) etc. Traditional algorithms can't recognize objects as efficiently due to its limitations. Whereas the deep learning models require large amount of data for training the dataset, which has more resource and labour intensive in nature. The selection of algorithm determines its precision in object detection as well as its reliability. The recognition and classification of object begins with preparing dataset followed by splitting the dataset into training dataset and test dataset. The task of training the dataset can be assisted by both traditional as well as modern deep neural networks. The loss per step or epoch is calculated on the training dataset to signify the efficiency and accuracy of the model. In this model, the loss per step is 2.73. We have achieved a maximum accuracy of about 85.18% after training the dataset used.

关键词： object detection Deep neural network Image segmentation Computer vision tensorflow object detection api Self-driving vehicle

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American Sign Language: detection and Automatic Text Generation 2

American Sign Language: Detection and Automatic Text Generat...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： Farhan, Youssef Ait Madi, Abdessalam Ryahi, Abdennour Derwich, Fatima Ibn Tofail Univ Natl Sch Appl Sci Adv Syst Engn Lab Kenitra Morocco CONFA ELECTRO Co Res & Dev Dept Meknes Morocco

ISBN: (纸本)9781665422093

In our daily life, communication is crucial. For people who are hard of hearing (deaf or/and mute) sign language is the means of their communication. Nonetheless, many people are still unaware of sign languages, resulting in a communication gap. To improve communication between deafmutes and the hearing majority, this paper proposes an ASL (American Sign Language) detection system for 26 alphabets and three assisted signs, which can detect ASL captured by a standard computer camera, then generate an automatic text. SSD-MobileNet is used as the object detection model. The proposed system achieved precision and recall of 82.8% and 85.5% respectively.

关键词： American Sign Language SSD-MobileNet tensorflow object detection api Computer Vision

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Artificial intelligent identification of apatite fission tracks based on machine learning

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045039页

作者： Ren, Zuoting Li, Shichao Xiao, Perry Yang, Xiaopeng Wang, Hongtao Jilin Univ Coll Earth Sci Changchun 130061 Peoples R China Minist Nat Resources Key Lab Mineral Resources Evaluat Northeast Asia Changchun 130061 Peoples R China London South Bank Univ Sch Engn London SE1 0AA England

Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth's uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the tensorflow object detection api, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.

关键词： apatite fission track OpenCV cascade classifier tensorflow object detection api precision recall F1-Score

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detection of weapons using Efficient Net and Yolo v3

Detection of weapons using Efficient Net and Yolo v3

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Ortiz Ramon, Anthony Barba Guaman, Luis Univ Tecn Particular Loja Artificial Intelligent Lab Loja Ecuador

ISBN: (纸本)9781728188645

With only 9% of the world's population, Latin America has one of the highest rates of violence in the world, generating insecurity, crime, robberies, weapons and homicides. In this project we worked with object detection to detect various types of weapons in public spaces such as stores, ATMs, streets, among others. Several trainings with different data sets and different neural network models were evaluated on the plataform Google colaboraty. Two models were used for training, Yolo v3 and Efficient D0, the models were trained with four categories of firearms;pistol, submachine gun, shotgun and rifle. The results of the experiments show that Yolo v3 is the best network for detecting firearms with an accuracy of 0.80 out of 1.

关键词： Convolutional Neural Network detection Weapons Yolov3 tensorflow object detection api Deep Learning

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object detection In Refrigerators Using tensorflow

Object Detection In Refrigerators Using Tensorflow

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作者： Kirti Agarwal University of Victoria

学位级别：硕士

object detection is widely used in many applications such as face detection, detecting vehicles and pedestrians on streets, and autonomous vehicles. object detection not only includes recognizing and classifying objects in an image, but also localizes those objects and draws bounding boxes around them. Therefore, most of the successful object detection networks make use of neural network based image classifiers in conjunction with object detection techniques. tensorflow object detection api, an open source framework based on Google's tensorflow, allows us to create, train and deploy object detection models. This thesis mainly focuses on detecting objects kept in a refrigerator. To facilitate the object detection in a refrigerator, we have used tensorflow object detection api to train and evaluate models such as SSD-MobileNet-v2, Faster R-CNN-ResNet-101, and R-FCN-ResNet-101. The models are tested as a) a pre-trained model and b) a fine-tuned model devised by fine-tuning the existing models with a training dataset for eight food classes extracted from the ImageNet database. The models are evaluated on a test dataset for the same eight classes derived from the ImageNet database to infer which works best for our application. The results suggest that the performance of Faster R-CNN is the best on the test food dataset with a mAP score of 81.74%, followed by R-FCN with a mAP of 80.33% and SSD with a mAP of 76.39%. However, the time taken by SSD for detection is considerably less than the other two models which makes it a viable option for our objective. The results provide substantial evidence that the SSD model is the most suitable model for deploying object detection on mobile devices with an accuracy of 76.39%. Our methodology and results could potentially help other researchers to design a custom object detector and further enhance the precision for their datasets.

关键词： Machine Learning Deep Learning CNN Image Classification object detection tensorflow object detection api

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