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YOLO object Recognition Algorithm and &x201C;Buy-Sell Decision&x201D;Model Over 2D Candlestick Charts

IEEE ACCESS 2020年 8卷 91894-91915页

作者： Birogul, Serdar Temur, Gunay Kose, Utku Duzce Univ Dept Comp Engn Technol Fac TR-81620 Duzce Turkey Duzce Univ Inst Sci Dept Elect Elect & Comp Engn TR-81620 Duzce Turkey Suleyman Demirel Univ Engn Fac Dept Comp Engn TR-32100 Isparta Turkey

Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a & x201C;Buy-Sell & x201D;object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO;for the training, one-year candlestick charts belonging to the stocks traded on Borsa & x0130;stanbul (BIST) between 2000 & x2013;2018 were used. The model, which can make a & x201C;Buy-Sell & x201D;decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the & x201C;Buy-Sell & x201D;decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85 & x0025;while a 100 & x0025;profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.

关键词： Predictive models Investment Time series analysis Machine learning Visualization object recognition Data models YOLO object detection and classification decision support systems deep learning finance trend decision

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detection and classification of diseased pine trees with different levels of severity from UAV remote sensing images

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ECOLOGICAL INFORMATICS 2022年第0期72卷

作者： Hu, Gensheng Yao, Pan Wan, Mingzhu Bao, Wenxia Zeng, Weihui Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applica Hefei 230601 Peoples R China Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China

The accurate detection and classification of diseased pine trees with different levels of severity is important in terms of monitoring the growth of these trees and for preventing and controlling disease within pine forests. Our method combines a DDYOLOv5 with a ResNet50 network for detecting and classifying levels of pine tree disease from remote sensing UAV images. In this approach, images are preprocessed to increase the background diversity of the training samples, and efficient channel attention (ECA) and hybrid dilated convolution (HDC) modules are introduced to DDYOLOv5 to improve the detection accuracy. The ECA modules enable the network to focus on the characteristics of diseased pine trees, and solve the problem of low detection accuracy caused by the simi-larities in color and texture between diseased pine trees and the complex backgrounds. The HDC modules capture the contextual information of targets at different scales;they increase the receptive field to focus on targets of different sizes, and address the difficulty of detection caused by large variations in the shapes and sizes of diseased pine trees. In addition, a low confidence threshold is adopted to reduce missed detections and a ResNet50 classification network is applied to classify the detection results into different levels of severity, in order to reduce the number of false detections and improve the classification accuracy. Our experimental results show that the proposed method improves the precision by 13.55%, the recall by 5.06% and the F1-score by 9.71% on 8 test images compared with YOLOv5. Moreover, the detection and classification results from our approach show that it outperforms classical deep learning object detection methods such as Faster R-CNN and RetinaNet.

关键词： object detection and classification Diseased pine trees Disease severity Deep learning UAV remote sensing image

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Semi-Automatic Dataset Generation for object detection and Recognition and its Evaluation on Domestic Service Robots

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JOURNAL OF ROBOTICS AND MECHATRONICS 2020年第1期32卷 245-253页

作者： Ishida, Yutaro Tamukoh, Hakaru Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Wakamatsu Ku 2-4 Hibikino Kitakyushu Fukuoka 8080196 Japan

This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will be used as teaching signals by the deep neural network, are automatically generated, as the region and category of the object composited with the background image are known. This reduces the human workload for dataset generation. Experiment results showed that the proposed method reduced the time taken to generate a data unit from 167 s, when performed manually, to 0.58 s, i.e., by a factor of approximately 1/287. The dataset generated using the proposed method was used to train a deep neural network, which was then applied to a domestic service robot for evaluation. The robot was entered into the World Robot Challenge, in which, out of ten trials, it succeeded in touching the target object eight times and grasping it four times.

关键词： domestic service robot object detection and classification dataset generation RoboCup@Home World Robot Challenge

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Overview of key technologies of human-computer interaction

Overview of key technologies of human-computer interaction

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作者： Yuyang Cheng Benjamin Fang Kai Lu Xingyu Wei Yifan Wang Michael Yang Xingchen Zhou Zhenrong Zhang University of electronic science and technology of China University of science and technology of China Zhejiang University University of Manchester

In the 21st century,computers are developing faster and ***-computer interaction(HCL) is defined as a multidisciplinary field of study which focuses on the interaction between humans and computer *** advancement of HCL makes a great contribution to nowadays' society as human beings gradually perceive themselves in a technological world that is in a time of high-speed *** situation of human-computer interaction is becoming more and more common,and computers have more and more intelligent *** latest computer technology has been applied to various disciplines,which have developed rapidly and achieved remarkable research *** this paper,we summarizes the core technologies involved in humancomputer interaction(HCI),mainly including human-computer interaction psychology,flexible screen technology,target recognition and classification,and expounds the current situation and development trend of these technologies.

关键词： Human-computer interaction Cognitive psychology Flexible screen object detection and classification

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A system for automatic rice disease detection from rice paddy images serviced via a Chatbot

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 185卷 106156-106156页

作者： Temniranrat, Pitchayagan Kiratiratanapruk, Kantip Kitvimonrat, Apichon Sinthupinyo, Wasin Patarapuwadol, Sujin Natl Elect & Comp Technol Ctr NECTEC 112 Phahonyothin Rd Khlong Luang Dist 12120 Pathumthani Thailand Kasetsart Univ Dept Plant Pathol Fac Agr Kamphaeng Saen Nakhon Pathom Thailand

A LINE Bot System to diagnose rice diseases from actual paddy field images was developed and presented in this paper. It was easy-to-use and automatic system designed to help rice farmers improve the rice yield and quality. The targeted images were taken from the actual paddy environment without special sample preparation. We used a deep learning neural networks technique to detect rice diseases from the images. We developed an object detection model training and refinement process to improve the performance of our previous research on rice leave diseases detection. The process was based on analyzing the model's predictive results and could be repeatedly used to improve the quality of the database in the next training of the model. The deployment model for our LINE Bot system was created from the selected best performance technique in our previous paper, YOLOv3, trained by refined training data set. The performance of the deployment model was measured on 5 target classes and found that the Average True Positive Point improved from 91.1% in the previous paper to 95.6% in this study. Therefore, we used this deployment model for Rice Disease LINE Bot system. Our system worked automatically real-time to suggest primary diagnosis results to the users in the LINE group, which included rice farmers and rice disease specialists. They could communicate freely via chat. In the real LINE Bot deployment, the model's performance was measured by our own defined measurement Average True Positive Point and was found to be an average of 78.86%. The system was fast and took only 2-3 s for detection process in our system server.

关键词： Image processing Rice disease Deep learning object detection and classification Chatbot LINE Bot Neural network applications

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Automating Inspection of Moveable Lane Barrier for Auckland Harbour Bridge Traffic Safety 27th

Automating Inspection of Moveable Lane Barrier for Auckland ...

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27th International Conference on Neural Information Processing

作者： Bacic, Boris Rathee, Munish Pears, Russel AUT Univ Engn Comp & Math Sci Auckland New Zealand

ISBN: (纸本)9783030638290;9783030638306

A moveable lane barrier along the Auckland Harbour Bridge (AHB) enables two-way traffic flow optimisation and control. However, the AHB barrier transfer machines are not equipped with an automated solution for screening of the pins that link the barrier segments. To improve traffic safety, the aim of this paper is to combine traditional machine with deep learning approaches to aid visual pin inspection. For model training with imbalanced dataset, we have included additional synthetic frames depicting unsafe pin positions produced from collected videos. Preliminary experiments on produced models indicate that we are able to identify unsafe pin positions with precision and recall up to 0.995. To improve traffic safety beyond the AHB case study, future developments will include extended datasets to produce near-real time IoT alerting solutions using mobile and other video sources.

关键词： Deep learning Machine learning Video surveillance object detection and classification TensorFlow Transfer learning

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Using Deep Learning Techniques to Detect Rice Diseases from Images of Rice Fields 33rd

Using Deep Learning Techniques to Detect Rice Diseases from ...

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Kiratiratanapruk, Kantip Temniranrat, Pitchayagan Kitvimonrat, Apichon Sinthupinyo, Wasin Patarapuwadol, Sujin Natl Elect & Comp Technol Ctr NECTEC Pathum Thani Thailand Kasetsart Univ Fac Agr Kamphaeng Saen Dept Plant Pathol Kamphaeng Saen Nakhon Pathom Thailand

ISBN: (纸本)9783030557881;9783030557898

Rice is a staple food feeding more than half of the world's population. Rice disease is one of the major problems affecting rice production. Machine Vision Technology has been used to help develop agricultural production, both in terms of quality and quantity. In this study, convolutional neural network (CNN) was applied to detect and identify diseases in images. We studied 6 varieties of major rice diseases, including blast, bacterial leaf blight, brown spot, narrow brown spot, bacterial leaf streak and rice ragged stunt virus disease. Our studied used well known pre-trained models namely Faster R-CNN, RetinaNet, YOLOv3 and Mask RCNN, and compared their detection performance. The database of rice diseases used in our study contained photographs of rice leaves taken from fields of planting areas. The images were taken under natural uncontrolled environment. We conducted experiments to train and test each model using a total of 6,330 images. The experimental results showed that YOLOv3 provided the best performance in term of mean average precision (mAP) at 79.19% in the detection and classification of rice leaf diseases. The precision obtained from Mask RCNN, Faster R-CNN, and RetinaNet was at 75.92%, 70.96%, and 36.11%, respectively.

关键词： Image processing Rice disease Deep learning object detection and classification

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Ball-Grid-Array Chip Defects detection and classification Using Patch-based Modified YOLOv3 24

Ball-Grid-Array Chip Defects Detection and Classification Us...

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International Conference on Technologies and Applications of Artficial Intelligence (TAAI)

作者： Phong-Phu Le Guo, Shu-Mei Chen, Ju-Chin Lien, Jenn-Jier James Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781728146669

The tiny size of defects and the noise information in the Ball-Grid-Array (BGA) Chip images have been challenging the image-processing based visual inspection systems in the Integrated Circuit (IC) manufacturing industry. Moreover, the gradient vanishing and high time-consuming problems of deep neural network models are a big obstacle for its application to solve industrial projects. This paper focuses on proposing a modified version of the YOLOv3 model, a leading object detection and classification method in terms of speed;and its application to deal with the problem of detecting and classifying defects on BGA Chip Images. There are five modifications constructed on YOLOv3 architecture that are aimed to enhance the ability of feature extraction towards small objects, strengthen the flow of information inside the network and eliminate the problem of redundant information. With the application of this model into BGA Chip Defects estimation, 49 patches (320x320 pixels), extracted from a single high-resolution BGA Chip image (1,450x1,450 pixels), are continuously fed into the modified YOLOv3 model to detect and classify the inner defects. As a result, the problem of BGA Chip Defects estimation is solved with the highest performance achieves an average precision of 86% at IoU (Intersection over Union) of 0.75 and an average recall of 99%.

关键词： object detection and classification Ball-Grid-Array Chip Defect YOLOv3 Defect detection

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Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters 18th

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18th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Chauhan, Vedang Joshi, Keyur D. Surgenor, Brian Queens Univ Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783319651729;9783319651712

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data for object recognition, classification and computer vision segmentation. Features are extracted from input data and used for object classification purposes. Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are popular tools for pattern recognition applications. The performance of the networks is usually defined in terms of the classification accuracy. However, there are no real design guidelines for training and testing protocols. This research set out to evaluate the effect on accuracy of the design parameters, including: size of the database, number of classes, quality of images, type of network, nature of training and testing strategy. A coin recognition task was used for the evaluation. A set of guidelines for part recognition tasks is presented based on experience with this task.

关键词： Pattern recognition Machine learning object detection and classification ANNs DNNs Testing protocols

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Image processing in aerial surveillance and reconnaissance: from pixels to understanding

Image processing in aerial surveillance and reconnaissance: ...

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Conference on Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII;and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing

作者： Dijk, Judith van Eekeren, Adam W. M. Rojas, Olga Rajadell Burghouts, Gertjan J. Schutte, Klamer TNO NL-2597 AK The Hague Netherlands

ISBN: (纸本)9780819497666

Surveillance and reconnaissance tasks are currently often performed using an airborne platform such as a UAV. The airborne platform can carry different sensors. EO/IR cameras can be used to view a certain area from above. To support the task from the sensor analyst, different image processing techniques can be applied on the data, both in real-time or for forensic applications. These algorithms aim at improving the data acquired to be able to detect objects or events and make an interpretation of those detections. There is a wide range of techniques that tackle these challenges and we group them in classes according to the goal they pursue (image enhancement, modeling the world object information, situation assessment). An overview of these different techniques and different concepts of operations for these techniques are presented in this paper.

关键词： Cameras Image processing enhancement object detection and classification interpretation

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