scholarly journals IoT Based Automatic Tyre Sorting System

2019 ◽  
Vol 8 (4) ◽  
pp. 10828-10832
Keyword(s):  
Image Processing ◽  
Manufacturing Industry ◽  
Research Work ◽  
Outer Core ◽  
Raspberry Pi ◽  
Process Time ◽  
Geometrical Parameters ◽  
Outer Diameter ◽  
Sorting Process ◽  
Sorting System

Tyre segregation is one of the indispensible processes in tyre manufacturing industry. In tyre manufacturing industry various size of tyres are examined at segregation unit at a time. Till today the tyre segregation process is done manually which increases the manpower and process time. Tyre sorting is the process of segregating the tyres from different sizes. The sorting process is based on the Geometrical parameter (Inner Diameter, Outer Diameter, Outer Core button Design) of the tyre. This research work is aimed to automate the sorting process of different tyres using Image processing and IOT. This pioneering work depicts a prototype of segregation system which includes the image processing segment to categorize the type of tyres which are fitted for various vehicles. The proposed system consist of Conveyor system, Raspberry pi -3 controller, tyre collecting bin, Servo motor and Image processing camera. This system camera monitors the incoming various tyres from the conveyor, based on the geometrical parameters of the tyres they are segregated and placed in the appropriate tyre collecting bin and the same information is shared to the database through IOT. The proposed model is observed to be very efficient with its counterpart.

scholarly journals Image-Processing-Based Low-Cost Fault Detection Solution for End-of-Line ECUs in Automotive Manufacturing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3520 ◽  
Author(s):  
Adrian Korodi ◽  
Denis Anitei ◽  
Alexandru Boitor ◽  
Ioan Silea
Keyword(s):  
Image Processing ◽  
Fault Detection ◽  
Experimental Model ◽  
Manufacturing Industry ◽  
Electronic Control Unit ◽  
Low Cost ◽  
Control Unit ◽  
Raspberry Pi ◽  
Automotive Manufacturing ◽  
The Impact

The manufacturing industry is continuously researching and developing strategies and solutions to increase product quality and to decrease production time and costs. The approach is always targeting more automated, traceable, and supervised production, minimizing the impact of the human factor. In the automotive industry, the Electronic Control Unit (ECU) manufacturing ends with complex testing, the End-of-Line (EoL) products being afterwards sent to client companies. This paper proposes an image-processing-based low-cost fault detection (IP-LC-FD) solution for the EoL ECUs, aiming for high-quality and fast detection. The IP-LC-FD solution approaches the problem of determining, on the manufacturing line, the correct mounting of the pins in the locations of each connector of the ECU module, respectively, other defects as missing or extra pins, damaged clips, or surface cracks. The IP-LC-FD system is a hardware–software structure, based on Raspberry Pi microcomputers, Pi cameras, respectively, Python and OpenCV environments. This paper presents the two main stages of the research, the experimental model, and the prototype. The rapid integration into the production line represented an important goal, meaning the accomplishment of the specific hard acceptance requirements regarding both performance and functionality. The solution was implemented and tested as an experimental model and prototype in a real industrial environment, proving excellent results.

scholarly journals Experimental Investigation on Bead Geometry Parameter and Mechanical Property of Part Fabricated by Wire Arc Additive Manufacturing

2021 ◽  
Author(s):  
Soundrapanidan Eswaran ◽  
◽  
Vivekkumar Panneerselvam ◽  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Manufacturing Industry ◽  
Research Work ◽  
Process Time ◽  
Material Strength ◽  
High Deposition Rate ◽  
Bead Geometry ◽  
Printed Materials ◽  
Wire Arc Additive Manufacturing

In additive manufacturing process, wire arc additive manufacturing process (WAAM) is a technique which can produce a metal 3D printed part. In Industries product are produced by wasting one third of its material, from this process time consumption and material wastage is more comparing in Subtractive Manufacturing over Additive Manufacturing. Additive Manufacturing stepped from 1925 in manufacturing industry and it has gained its remarkable growth in past few decades, as of now metal 3D oriented parts have come to play a major role in aerospace industry. This research work focused on Gas Metal Arc Welding (GMAW) welding. It has high deposition rate, ultimate build volume and good structural integrity compare with other additive manufacturing process. MACH3 controller is used to control the welding torch motion for addition of material by 3 axis movement (X, Y and Z). To identify the correct parameters for metal part we have done numbers of samples by changing values in the MIG machine from that we finalize the three parameters through visualizes on the printed materials after that a wall like structure is built and post processing like cutting the materials from base plate, grinding the uneven surface on printed materials. The printed materials are ready for material testing like bead geometry analysis of various parameter and tensile testing to identify the printed material strength, elongation, stress and strain.

scholarly journals Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

2022 ◽  
Vol 2022 ◽  
pp. 1-18
Author(s):  
Dereje Tekilu Aseffa ◽  
Harish Kalla ◽  
Satyasis Mishra
Keyword(s):  
Optimization Technique ◽  
Research Work ◽  
Optimization Techniques ◽  
Magnetic Sensors ◽  
Batch Size ◽  
Raspberry Pi ◽  
Prototype Development ◽  
Embedded Platform ◽  
Artificial Intelligence Technology ◽  
Banknote Recognition

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

scholarly journals HVAC and Audio Control System using Gesture Recognition in Cars

2020 ◽  
Vol 9 (3) ◽  
pp. 3193-3196
Keyword(s):  
Image Processing ◽  
Control System ◽  
Gesture Recognition ◽  
Background Subtraction ◽  
Advanced Technology ◽  
Raspberry Pi ◽  
Hvac System ◽  
Hand Gestures ◽  
Hand Part ◽  
Free Interaction

Gesture recognition technology entails a wide variety of touch-free interaction capabilities which controls notably contribute to easing our interaction with devices, reducing the need for a keys, or button. To recognize the different hand gestures for different control system in cars is done through image processing. A new method for the hand gestures is that, the hand part gets extracted from the background using background subtraction algorithm using raspberry pi, there is no need of buttons for using of some equipments in different vehicles by using an advanced technology. In gesture recognition technology we can control the audio and HVAC system automatically instead of searching for a particular button, which causes distraction while driving.

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

scholarly journals Analysis of Potholes on Road Using Image Processing

2021 ◽  
Vol 9 (8) ◽  
pp. 1731-1734
Author(s):  
K. S. Prasath
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Computer Science ◽  
Research Area ◽  
Raspberry Pi ◽  
Image Detection ◽  
The Road ◽  
Text Signals ◽  
Science Disciplines

Abstract: Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is one among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too. Image detection on road is primarily carried out with the help of camera with Raspberry pi 3 model b+ and stimulation software. The device is built in such a way that we can identify any potholes in the respective roads and able to rectify as soon as possible with the help of the device. The data signals shared by the device will be converted to text signals from which we can get it right. These devices are fixed at top of the lamppost which is located at the corners of the road from where the device is monitoring the road at 120 degree for weekly once respectively. Keywords: Image processing, Image detection on road, Raspberry pi 3, 120 degree

scholarly journals PERANCANGAN SISTEM DETEKSI OBJEK PADA ROBOT KRSBI BERBASIS MINI PC RASPBERRY PI 3

2019 ◽  
Vol 12 (1) ◽  
pp. 56-64
Author(s):  
Ilfan Sugianda ◽  
Thamrin Thamrin
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Raspberry Pi ◽  
Process Data ◽  
Single Board Computer ◽  
Object Based ◽  
Football Match ◽  
Omni Wheel ◽  
Python Programming ◽  
Camera Sensor

KRSBI Wheeled is One of the competitions on the Indonesian Robot Contest,. It is a football match that plays 3 robot full autonomous versus other teams. The robot uses a drive in the form of wheels that are controlled in such a way, to be able to do the work the robot uses a camera sensor mounted on the front of the robot, while for movement in the paper author uses 3 omni wheel so the robot can move in all directions to make it easier towards the ball object. For the purposes of image processing and input and output processing the author uses a Single Board Computer Raspberry PI 3 are programmed using the Python programming language with OpenCV image processing library, to optimize the work of Single Board Computer(SBC) Raspberry PI 3 Mini PC assisted by the Microcontroller Arduino Mega 2560. Both devices are connected serially via the USB port. Raspberry PI will process the image data obtained webcam camera input. Next, If the ball object can be detected the object's position coordinates will be encoded in character and sent to the Microcontroller Arduino Mega 2560. Furthermore, Arduino mega 2560 will process data to drive the motors so that can move towards the position of the ball object. Based on the data from the maximum distance test results that can be read by the camera sensor to be able to detect a ball object is �5 meters with a maximum viewing angle of 120 �.

scholarly journals Pengenalan Plat Nomor Menggunakan Image Processing Pada Perangkat Mikrokontroller

2019 ◽  
Vol 4 (2) ◽  
pp. 63-70
Author(s):  
Ribhanrio Humonggio ◽  
Riska Kurniyanto Abdullah ◽  
Muhammad Asri
Keyword(s):  
Image Processing ◽  
Raspberry Pi

Perancangan sistem pengenalan plat nomor merupakan salah satu sistem yang dapat membantu proses pengolahan data plat nomor kendaraan berupa plat mobil dengan menggunakan image processing yang dapat meningkatkan kinerja dari sistem kontrol dan informasi pada area pengenalan. Ada beberapa tahapan dalam pengenalan yaitu gambar yang diambil melalui kamera webcam yang sudah dikonfigurasikan dengan raspberry pi 3, selanjutnya mencari lokasi plat nomor dan menyegmentasi setiap karakter yang ada dari plat nomor tersebut, selanjutnya proses optical caracter recognition dapat mengenali huruf dan angka. Hasil pengujian menunjukkan tingkat keberhasilan yang cukup memuaskan, dari 12 hasil pengujian gambar plat nomor kendaraan total keberhasilan secara menyeluruh terdapat pada plat nomor dengan persentase akurasi 84.7% sampai 99.97 % dan pengenalan warna 8 kendaraan yang berhasil dikenali dengan benar dan 4 kendaraan salah. Pengujian kedua dilakukan pada plat kendaraan mobil mini dengan posisi pengambilan gambar berbeda, dimana hasil dari deteksi berhasil mengenali angka dan huruf dengan benar nilai akurasi 93.3%.

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