Image Recognition Module Research Articles

Image Inpainting based on Gated Convolution and spectral Normalization

Traditional image inpainting methods based on deep learning have the problem of insufficient discrimination between the missing area and the global area information in the feature extraction of image inpainting tasks because of the characteristics of the model constructed by the convolutional layer. At the same time, the traditional generative adversarial network often has problems such as training difficulties and model collapse in the training process. To solve the above problems and improve the repair effect of the model, this paper proposes a dual discriminator image inpainting model based on generative adversarial network combining gated convolution and spectral normalization. The model is mainly composed of an image inpainting module and an image recognition module. The traditional image inpainting model considers all input pixels as valid pixels when extracting the features of the image to be inpainted, which is unreasonable for the image inpainting task. In order to solve this problem, the gated convolution is designed to replace the role of traditional convolution in the image inpainting module. Gated convolutions address the irrationality of ordinary convolutions in image inpainting tasks by providing learnable dynamic feature selection mechanisms for each channel at each spatial location in all layers. At the same time, generative image inpainting models usually have problems such as training hard mode collapse during the training process. In this paper, we intend to introduce the spectral normalization mechanism in the convolutional layer design of the discriminator module. By introducing Lipschitz continuity constraints from the spectral norm of the parameter matrix of each layer of the neural network, the neural network is better insensitive to input perturbations, making the training process more stable and easier to converge. It solves the problems of mode collapse and model training difficulty in the training of image inpainting model based on generative adversarial network. Finally, qualitative and quantitative experiments show that the image inpainting model based on gated convolution and spectral normalization solves the above problems, and the inpainted image has reasonable texture structure and contextual semantic information.

Auto-Tracking Wireless Power Transfer System With Focused-Beam Phased Array

This article used image recognition and beam forming technology to build a phased array with target tracking and transmitting the microwaves with a focused beam. The coordinate position of the target is obtained from the image recognition module and converted into phase information for the phased array. This system was constructed by a 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 4 5.764-GHz phased array with four 4-bit phase shifters. The phase shifters created a focused beam, which requires not only the target’s direction angle but also the transmission distance. The target position as well as direction and distance information were gathered using image recognition. The tracking beam method was evaluated by simulation and actual measurement. The results showed that the focused beam can always be formed in real time to track the target. The transfer efficiency of the focused beam was improved higher than that of uniform phase beam within a distance of 50 cm. The automatic tracking power transmission system has a response time of about 100 ms.

Design of field real-time target spraying system based on improved YOLOv5.

Deep learning techniques have made great progress in the field of target detection in recent years, making it possible to accurately identify plants in complex environments in agricultural fields. This project combines deep learning algorithms with spraying technology to design a machine vision precision real-time targeting spraying system for field scenarios. Firstly, the overall structure scheme of the system consisting of image acquisition and recognition module, electronically controlled spray module and pressure-stabilized pesticide supply module was proposed. After that, based on the target detection model YOLOv5s, the model is lightened and improved by replacing the backbone network and adding an attention mechanism. Based on this, a grille decision control algorithm for solenoid valve group on-off was designed, while common malignant weeds were selected as objects to produce data sets and complete model training. Finally, the deployment of the hardware system and detection model on the electric spray bar sprayer was completed, and field trials were conducted at different speeds. The experimental results show that the improved algorithm reduces the model size to 53.57% of the original model with less impact on mAP accuracy, improves FPS by 18.16%. The accuracy of on-target spraying at 2km/h, 3km/h and 4km/h speeds were 90.80%, 86.20% and 79.61%, respectively, and the spraying hit rate decreased as the operating speed increased. Among the hit rate components, the effective recognition rate was significantly affected by speed, while the relative recognition hit rate was less affected.

Automatic Correcting Mathematics Operating System based on AI

With the development of society, the advancement of science and technology has become an important basis for whether a country is strong or not, and the development of science and technology cannot do without a solid mathematical foundation. Mathematics is the most important is to calculate, and the enhancement of computing power is to need constant practice to do the problem and correct the wrong problem, so this design of an ai-based automatic correction math homework system. The design of the system can be divided into two parts, the front user operating platform interface design and background function design. The function realization of the background can be divided into five modules, which are image recognition module, mathematical operation module, answer comparison module, data interaction module and image processing module. The image recognition module is used to recognize the text in the picture and obtain the coordinates. The mathematical operation module is used to carry on the operation to the identified problem; The answer comparison module is used to judge right and wrong; The data interaction module USES MySQL database for data interaction. Image processing module is used to identify right and wrong.

A multi–modal unsupervised fault detection system based on power signals and thermal imaging via deep AutoEncoder neural network

In this paper a multi-modal unsupervised Deep Learning based algorithm for fault detection is proposed. Such method is applied to real data from a testing procedure implemented on an industrial production line. Both thermal images and current and power measurements coming from industrial refrigerators are collected. The considered dataset is highly unbalanced with the vast majority of samples being healthy. Thermal images are processed via a Deep Convolutional neural network. The features extracted from the thermal images are thus merged to structured data of power, current and temperature. Therefore, a Deep Auto-Encoder is trained on the dataset to signal anomalies corresponding to faults in the refrigerators. Three different methods are trained and compared: (1) an automatic method in which an expert extracts relevant features from thermal images without using the image recognition module; (2) a semi-automatic method where the convolutional neural network is applied to regions of interest within the thermal images selected by an expert operator; (3) a fully automatic method in which the Deep convolutional network processes the whole thermal image without any human intervention. The three methods show comparable results with nevertheless slight differences.

Design of Intelligent Security Early warning system for unguarded railway crossing in mining area

There are many railway crossings in the mine, but there are not enough people to keep every crossing manned. For the traditional unguarded railway crossing, we set up a warning sign at a certain distance from the crossing to remind pedestrians and drivers. For some crossings with poor vision, both pedestrians and locomotive drivers at the crossings can’t make timely and accurate judgment on the surrounding real-time road conditions, which is easy to cause safety accidents. In order to solve this technical problem and demand, an intelligent security and early warning robot with image recognition module and intelligent control board was developed by combining advanced theories and technologies such as Internet +, image recognition, beidou+ GPS dual-mode positioning, wind and solar complementary power generation and multi-sensor fusion. The system can improve the intelligent level of the unguarded crossing in the mining area, realize the unmanned or less unmanned crossing work, and ensure the safety of railway transportation in the coal mine.

Research and Implementation of Robot Path Planning Based on Computer Image Recognition Technology

This paper focuses on the image processing and recognition module. The path planning module is only a simple algorithm research and implementation, the current system research needs to continue to improve, especially the path planning module. This paper studies the robot path planning based on image recognition, and on the robot contest platform to do simulation experiment, but in the actual application of robot environment is complicated, the robot camera collected images may be part contains several pieces of information, or part of the image and the other robot, the robot path planning of the how to do it. Moreover, the robot may be beyond our set range, and the dynamic path planning algorithm cannot guarantee the absolute acquisition of the optimal strategy. Therefore, how to improve the existing system, improve the accuracy of image recognition and improve the path planning algorithm is one of the important research work in the future.

PeppeRecycle: Improving Children\u2019s Attitude Toward Recycling by Playing with a Social Robot

In this paper, we investigate the use of a social robot as an engaging interface of a serious game intended to make children more aware and well disposed towards waste recycle. The game has been designed as a competition between the robot Pepper and a child. During the game, the robot simultaneously challenges and teaches the child how to recycle waste materials. To endow the robot with the capability to play as a game opponent in a real-world context, it is equipped with an image recognition module based on a Convolutional Neural Network to detect and classify the waste material as a child would do, i.e. by simply looking at it. A formal experiment involving 51 primary school students is carried out to evaluate the effectiveness of the game in terms of different factors such as the interaction with the robot, the users’ cognitive and affective dimensions towards ecological sustainability, and the propensity to recycle. The obtained results are encouraging and draw promising scenarios for educational robotics in changing children’s attitudes toward recycling. Indeed Pepper turns out to be positively evaluated by children as a trustful and believable companion and this allows children to be concentrated on the “memorization” task during the game. Moreover, the use of real objects as waste items during the game turns out to be a successful approach not only for perceived learning effectiveness but also for the children’s engagement.

A robust iris localization scheme for the iris recognition

Due to current security situations around the globe, iris biometric technology is highly preferred for both overt and covert applications. A typical iris biometric system includes image acquisition, iris segmentation, features extraction, and matching and recognition modules. Amongst these modules, iris segmentation plays a decisive role because it segments the valid iris part in an input eyeimage. It includes two tasks: iris localization and noise (e.g., eyelids) removal. Notably, the overall performance of an iris biometric system strongly relies on the iris localization task, because it demarcates the actual iris contours. Some contemporary iris localization schemes search over a three-dimensional (3D) space while marking iris boundaries, which is a time-consuming process if not optimized properly. Besides, some schemes also resort to the fixed and/or crude thresholding-based techniques for pupil localization. Notably, such schemes may perform poorly if image data do not maintain quality. To address these issues, this study proposes a robust iris localization scheme maintaining both speed and accuracy. It includes preprocessing the input eyeimage using an order statistic-filter and the bilinear interpolation scheme, extracting an adaptive threshold using the image’s histogram, processing binary image via the morphological operators, extracting pupil’s center and radius based on the centroid and geometry concepts, marking iris outer boundary using the Circular Hough transform (CHT) and refining coarse iris boundaries through the Fourier series. The proposed scheme exhibits relatively better experimental results compared with some contemporary iris localization schemes on the public iris databases: IITD V1.0, CASIA-Iris-Interval and MMU V1.0.

Research on language analysis of English translation system based on fuzzy algorithm

In recent years, with the rapid development of computer technology, the need for barrier-free communication between people of all countries have become more and more urgent. Therefore, it is extremely important to establish a high translation accuracy and high-quality English translation system. At present, Although the various English translation systems on the market have solved the communication problems between different languages to a certain extent, there are a series of problems such as language translation ambiguity and inaccurate use of words in translation methods, in order to improve English The translation accuracy of the translation system can improve the quality of the English translation system. This paper proposes a language analysis study of the English translation system based on fuzzy algorithms. The research of this paper firmly grasps the analysis and understanding of the language, analyzes it from the corpus, vocabulary, syntax, and translation characteristics, and fully understands its language characteristics, so as to eliminate the semantic understanding ambiguity in the translation process to a certain extent. Thereby improving the accuracy of the translation. The English translation system designed in this paper includes an image input module and an image recognition module, so the Gaussian blur algorithm is used for processing. The Gaussian blur algorithm can retain edge information in the edge area where the pixel value of the image changes sharply, and can effectively remove noise and enhance the image effect. Therefore, this article uses fuzzy algorithm-based English translation system language analysis research, first analyze the English language characteristics, and then use Gaussian fuzzy algorithm to denoise the image in the translation system, and then display the image recognition results.

Design of human-like behavior learning machine based on neural network FPGA

Neural network has been widely used in image recognition, because it can imitate the behavior of biological visual nerve to obtain high recognition accuracy. Aiming at image recognition, this paper studies the machine self-learning system of image recognition results based on neural network FPGA, designs the data sensing and image recognition processing module based on neural network, and studies the key problems of the system self-recognition and machine self-learning process. The image recognition module uses bionics and parallax principle to get the synchronous exposure image of surrounding scenery, reconstruct the three-dimensional shape and position of surrounding objects, and get the information needed by the system design through image processing algorithm. On the basis of this module, a machine self-learning algorithm based on the result of image recognition is designed to build a knowledge base for the machine and guide the machine by modifying the weight and making decision to realize human-like behavior of machine self-learning. The experimental results show that the recognition accuracy of dynamic image recognition in image recognition network is about 80% compared with the corresponding static image recognition, and the time and power consumption are reduced by 24%, 27.8% and 41% respectively.

Security and Privacy of Smart Home Systems Based on the Internet of Things and Stereo Matching Algorithms

With the rapid development of the Internet of Things (IoT), security and privacy of smart home systems based on IoT are more and more popular. As the key component of IoT, wireless communication and sensor technology are prerequisites for the security and confidentiality of smart home systems. The smart home systems integrate electronic information technology and computer control. By designing and installing various sensors in the home for collecting data, and then using the IoT platform for data transmission, the remote control of the home running state can be realized. The home security is guaranteed. This article designs IoT architecture of smart home, and then hardware and software are designed according to the system architecture. The hardware part is mainly analyzed from the image recognition module and the speech recognition module. In addition, a stereo matching algorithm for smart video surveillance is proposed to optimize the accuracy of the surveillance system. Finally, the simulation results prove that the designed smart home systems have a low cost and high accuracy. It not only optimizes the performance of smart home systems but also improves the safety factor.

FishEye - An Integrated Marine Species' Visualization

Despite the historical limitations associated with the study of marine species, current technology makes it possible to collect ocean animal data in a more accessible way, with a variety of tagging and tracking devices. Hence, such information is nowadays generated in large amounts, often in textual formats, making it difficult to interpret and analyze. Information visualization, due to its potential to represent large amounts of data while alleviating cognitive load associated with data interpretation, may help overcome this limitation. This article presents the visualization of a marine species that allows the representation and interactive exploration of species' telemetric data through an integrated dashboard with coordinated views. A species image recognition module was implemented together with the described visualization, enabling species recognition. Usability tests have validated its potential in making important patterns immediately perceivable and also showed that FishEye provides exploration and comparison mechanisms to obtain further relevant information.

Design of mobile augmented reality game based on image recognition

The work studied a complete set of development system for mobile augmented reality game based on combination of AR technology and RTS game. An effective image recognition strategy was proposed through SIFT feature matching algorithm. Integrated with cloud image recognition module, the response speed of image recognition module was improved by eliminating error matching point. Through optimization of the scheme and existing technologies, the improved image recognition algorithm was applied to the game system, realizing RTS game system on mobile intelligent terminal. The experimental results showed that the proposed method meets the requirement of augmented reality system in terms of efficiency and accuracy. In addition, the demand of users for information expansion can be satisfied by the method to some extent, which is more applicable than traditional augmented reality.

2A1-G20 次世代知能化視覚モジュールを搭載した5眼ヒューマノイドによる人注意観察機能の実現

Three different levels of attention is realized: broad attention to whole environment, detail attention for the part of the focused human, and realtime attention for the 3D motion of the whole body of the focused human. We developed the humanoid head with wide viewing angle and high visual acuity by using five cameras and Image Recognition Module. RT-Middleware enables several simple functions to work together very easily. With the systems, the humanoid can observe human attention efficiently.

2P1-J20 e-nuvoをベースとしたロボカップ用ヒューマノイドロボットの設計製作(ロボカップ・ロボットコンテスト)

We started to participate in RoboCup humanoid league. Based on the previous experience, more lightweight robots are needed for this application. Then we adopted a commercial legged robot "e-nuvo" by ZMP Inc., and added two arms as well as a camera, a board PC and batteries. Total weight of the new humanoid robot is 1.8kg, which is only 0.5kg bigger than the weight of the original e-nuvo. Two arms are driven by the same processor with other leg angles and incorporated in the same architecture, so that the arms are controlled in the same software system. We adopted OpenCV on Windows for the image processing library, and implemented image recognition modules to detect ball and goal positions. We are now developing the total software system that is sufficient for the RoboCup competition.

1P1-C04 画像認識モジュール向け画像処理コンポーネントの開発(RTミドルウェアとオープンシステム)

1P1-C04 画像認識モジュール向け画像処理コンポーネントの開発(RTミドルウェアとオープンシステム)

DEVELOPMENT OF A SURGICAL NAVIGATION ROBOT USING IMAGE SERVO TRACKING TECHNIQUES

This paper presents the development of surgical navigation robot, which is designed based on the image servo tracking technique. The surgical navigation robot plays an important role to the computer aided surgery (CAS), since it provides the patient's space coordinate during surgical navigation stage in real-time. Especially, the proposed active image servo tracking based surgical navigation robot can track the surgical marker that is mounted on the patient's hard tissue of the surgical position, so that the navigation space can be automatically extended. The proposed surgical navigation robot system consists of the in-lab designed dual-axis eye-in-hand robot (EIHR), digital signal processor (DSP) based EIHR motion controller, image servo tracking and positioning software. The EIHR robot is composed of the dual-axis robot mechanics and two CCD cameras, which acts as the stereoscopic image capture platform. The DSP based EIHR motion controller is designed to control the EIHR robot in a desired velocity, so that the surgical marker can be dynamically tracked. The image servo tracking and positioning software is the kernel of this work, and it is composed of image capture and recognition module, fuzzy logic based surgical marker tracking module and surgical marker positioning module. The image capture and recognition module is developed to abstract the image features of the surgical marker. The intelligent fuzzy logic based surgical marker tracking module is capable of generating robot's control command so that the surgical marker can be tracked within the desired target window. In addition, the surgical marker positioning module is used to calculate the surgical marker's space coordinates in terms of the features of stereoscopic images and the robot's posture. Finally, the prototype had been finished, and the experiment results showed that proposed EIHR achieved 1.7 mm position error within 500 mm operation range.

A Study on Recognition and Understanding of Mechanical Drawings (Representation and Rule-Based Understanding of Individual Projection).

An intelligent translating system which is under development extracts the CAD (Computer-Aided Design) data from mechanical drawings. The system consists of three modules: image processing, image recognition and image understanding. By scanning the geometric structure of the drawing, the image recognition module tries to identify visible lines, dimension lines, extension lines, broken lines and center lines. In the image understanding module, vector data are modeled on the chemical bond. Feature points and vectors are interpreted as atoms and molecules, respectively, and the degree of understanding is equivalent to the coupling force. The representation of the drawing is a hierarchical structure composed of end points, segments of lines, and front, side, and top views. Expert rules detect and correct errors in recognition to arrive at complete understanding.