Automatic optical inspection for detecting keycaps misplacement using Tesseract optical character recognition

This research study aims to develop automatic optical inspection (AOI) for detecting keycaps misplacement on the keyboard. The AOI hardware has been designed using an industrial camera with an additional mechanical jig and lighting system. Optical character recognition (OCR) using the Tesseract OCR engine is the proposed method to detect keycaps misplacement. In addition, captured images were cropped using a predefined region of interest (ROI) during the setup. Subsequently, the cropped ROIs were processed to acquire binary images. Furthermore, Tesseract processed these binary images to recognize the text on the keycaps. Keycaps misplacement could be identified by comparing the predicted text with the actual text on the golden sample. Experiments on 25 defects and 25 non-defected samples provided a classification accuracy of 97.34%, a precision of 100%, and a recall of 90.70%. Meanwhile, the character error rate (CER) obtained from the test on a total of 57 characters provided a performance of 10.53%. This outcome has implications for developing AOI for various keyboard products. In addition, the precision level of 100% signifies that the proposed method always offers correct results in detecting product defects. Such outcomes are critical in industrial applications to prevent defective products from circulating in the market

A Fast and Accurate Object Detection Algorithm on Humanoid Marathon Robot

This paper introduces a fast and accurate object detection algorithm based on a convolutional neural network for humanoid marathon robot applications. The algorithm is capable of operating on a low-performance CPU without relying on the GPU or hardware accelerator. A new region proposal algorithm, based on color segmentation, is proposed to extract a region containing a potential object. As a classifier, the convolution neural network is used to predict object classes from the proposed region. In the training phase, the classifier is trained with an Adam optimizer to minimize the loss function, using datasets collected from humanoid marathon competitions and diversified using image augmentation. An NVIDIA GTX 1070 training machine, with 500 batch images per epoch and a learning rate of 0.001, required 12 seconds to minimize the loss value below 0.0374. In the accuracy evaluation, the proposed method successfully recognizes and localizes three classes of marker with a training accuracy of 99.929%, validation accuracy of 99.924%, and test accuracy of 98.821%. As a real-time benchmark, the algorithm achieves 41.13 FPS while running on a robot computer with Intel i3-5010U CPU @ 2.10GHz

Desain Sistem Pengatur Lampu Lalu Lintas dengan Identifikasi Kepadatan Kendaraan Menggunakan Metode Subtraction

The increasing number of vehicle causes the increasing of traffic density in which one of the main factors of congestion. Traffic density is usually alocated at certain points of roads, one at the instersection. In the novel technology, traffic in the crossroads had been controlled by traffic light using a traffic density prediction system. This prediction system would determine the duration of active green lights and red lights at each intersection. One of the most common prediction systems is a statistical estimation of vehicle density. Other method at controlling traffic density such as visually monitoring system might be implemented to increase system performance. Therefore, this research proposes an automatically traffic control system by predicting traffic density using image processing techniques. The proposed system is using a camera to visually monitor traffic condition. The image data obtained from the camera would be processed using an image processing and background subtraction techniques. This technique compared an the captured-image with a reference image to result a subtracted-image depicted the traffic density which is represented by the number of white pixels. Based on the number of white pixels that have been obtained, the percentage of vehicle queue length and vehicle density can be determine. The percentage then sent to the microcontroller in order to control the duration of the active green light. The traffic light control system using traffic density calculation has an accuracy of up to 77.03% while using the calculation of vehicle queue length reached 91.18%

Desain Sistem Pengatur Lampu Lalu Lintas dengan Identifikasi Kepadatan Kendaraan Menggunakan Metode Subtraction

The increasing number of vehicle causes the increasing of traffic density in which one of the main factors of congestion. Traffic density is usually alocated at certain points of roads, one at the instersection. In the novel technology, traffic in the crossroads had been controlled by traffic light using a traffic density prediction system. This prediction system would determine the duration of  active green lights and red lights at each intersection. One of the most common prediction systems is a statistical estimation of vehicle density. Other method at controlling traffic density such as visually monitoring system might be implemented to increase system performance. Therefore, this research proposes an automatically traffic control system by predicting traffic density using image processing techniques. The proposed system is using a camera to visually monitor traffic condition. The image data obtained from the camera would be processed using an image processing and background subtraction techniques. This technique compared an the captured-image with a reference image to result a subtracted-image depicted the traffic density which is represented by the number of white pixels. Based on the number of white pixels that have been obtained, the percentage of vehicle queue length and vehicle density can be determine. The percentage then sent to the microcontroller in order to control the duration of the active green light. The traffic light control system using traffic density calculation has an accuracy of up to 77.03% while using the calculation of vehicle queue length reached 91.18%.Keywords : Image processing, image subtraction, light control system, traffic density, dilation, erosionAbstrakBertambahnya jumlah kendaraan menyebabkan meningkatnya kepadatan lalu lintas yang menjadi salah satu faktor utama penyebab kemacetan. Kepadatan lalu lintas biasanya teralokasi di beberapa titik-titik tertentu di ruas jalan, salah satunya di persimpangan. Saat ini lalu lintas di persimpangan jalan diatur oleh lampu lalu lintas menggunakan sistem prediksi kepadatan lalu lintas. Sistem prediksi ini nantinya akan menentukan lama aktifnya lampu hijau dan lampu merah di setiap persimpangan. Salah satu sistem prediksi yang banyak digunakan adalah metode estimasi stastistik kepadatan kendaraan. Metode lain pengontrolan kepadatan lalu lintas seperti sistem pemantauan secara visual memungkinkan untuk diterapkan guna menambah performansi sistem. Untuk itu penelitian ini mengusulkan pembuatan sebuah sistem pengontrolan lampu lalu lintas secara otomatis dengan prediksi kepadatan kendaraan menggunakan teknik pengolahan citra. Sistem yang dibangun menggunakan kamera untuk memantau kondisi kendaraan di jalan raya. Data gambar yang didapat dari kamera kemudian diolah menggunakan teknik pengolahan citra dan teknik pengurangan citra. Teknik ini membandingkan citra objek dengan citra referensi sehingga dapat diketahui jumlah piksel putih pada citra hasil pengurangan citra. Berdasarkan jumlah piksel putih yang telah diperoleh tersebut dapat diketahui persentase panjang antrian kendaraan dan kepadatan kendaraan. Data persentase yang diperoleh kemudian dikirim ke mikrokontroler untuk mengontrol durasi nyala lampu hijau. Pengontrolan lampu lalu lintas dengan perhitungan kepadatan kendaraan memiliki akurasi hingga 77.03% sedangkan dengan perhitungan panjang antrian kendaraan mencapai 91.18%. Kata Kunci : Pengolahan citra, pengurangan citra, sistem kontrol lampu, kepadatan kendaraan, dilation, erosio

Tiny-YOLO distance measurement and object detection coordination system for the BarelangFC robot

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

ABSTRAKTujuan dari penelitian ini adalah untuk mendeteksi objek dan mengestimasi pose objek menggunakan kamera RGB-D. Dalam penelitian ini, kami mengusulkan pemrosesan data pada citra RGB dan citra depth saja, tanpa menggunakan point cloud, seperti pada umumnya. Metode yang diusulkan mendeteksi posisi dan orientasi objek menggunakan DRBox-v2 dari Region of Interest (ROI), yang sebelumnya diperoleh dari pendeteksian pada penanda ArUco. Hasil deteksi objek kemudian diskalakan dan digunakan pada citra depth untuk mendapatkan perkiraan posisi dan orientasi objek. Dari sisi pendeteksi objek, usulan metode memperoleh nilai Average Precision (AP) sebesar 0,740. Sedangkan untuk estimator pose, usulan metode menghasilkan kesalahan posisi rata-rata 13,36 mm dan kesalahan orientasi rata-rata 0,75 derajat. Metode yang diusulkan berpotensi menjadi alternatif sistem deteksi objek dan estimasi pose pada kamera RGB-D yang tidak memerlukan pemrosesan point cloud dan tidak memerlukan model referensi objek.Kata kunci: deteksi objek, estimasi pose, DRBox, ArUco, bin-picking ABSTRACTThis study aims to detect objects and estimate the object's pose using an RGB-D camera. In this study, we proposed data processing on RGB images and depth images only, without using point clouds, as in general. The proposed method detected the object's position and orientation using the DRBox-v2 from the Region of Interest (ROI), which was previously obtained from detecting ArUco markers. The object detection results were then scaled and used in the depth image to get the object's approximate position and orientation. In object detection, the proposed method obtained an Average Precision (AP) value of 0.740. As for the pose estimator, our method generated an average position error of 13.36 mm and an average orientation error of 0.75 degrees. Therefore, this method can be an alternative object detection and pose estimation system on an RGB-D camera that does not require point cloud processing and an object reference model.Keywords: object detection, pose estimation, DRBox, ArUco, bin-pickin

Workshop Teknologi Robotika untuk Anak Usia 8-15 Tahun di Kota Batam

The Covid-19 pandemic requires elementary and junior high school students to do online learning activities at home. In practice, children interact more often with their gadget during online school hours and outside these hours. Its a concern for most Graha Nusa Batam community residents because the children there are already starting to have symptoms of gadget addiction. Against this background, Batam State Polytechnic (Polibatam) held a robotics technology workshop program for children aged 8-15 years. This activity aims to increase children's understanding of robotics technology, which includes electronics and mechanics. In addition, this activity provides several benefits, namely: being an attractive alternative for children to reduce playing with their gadgets; provide children's experiences in problem solving, creativity and collaboration; and include field experience for Polibatam students by applying the knowledge gained through Project Based Learning (PBL) for the benefit of the community.This workshop held four meetings (March to April 2021) at the At-Taqwa Al-Qur'an Education Park (TPA) building, Graha Nusa Batam housing. The robot used is an analog line follower. Pre-test and post-test were conducted to measure the increase in participants' understanding of the material. This test shows that participants' knowledge of robotics technology can increase up to 25% for participants aged 8-11 years and 22% for 12-15 years.Masa pandemi Covid-19 mengharuskan anak-anak siswa sekolah dasar dan menengah melaksanakan aktivitas pembelajaran secara daring di rumah. Dalam praktiknya, anak-anak menjadi lebih sering berinteraksi dengan gawai, baik pada jam sekolah daring maupun di luar jam tersebut. Hal ini menjadi kekhawatiran sebagian besar masyarakat perumahan Graha Nusa Batam, sebab anak-anak di sana sudah mulai memiliki gejala kecanduan gawai. Dengan latar belakang permasalahan tersebut, tim dari Politeknik Negeri Batam (Polibatam) mengadakan program workshop teknologi robotika untuk anak usia 8-15 tahun. Tujuan kegiatan ini adalah meningkatkan pemahaman anak mengenai teknologi robotika yan meliputi elektronika dan mekanika. Selain tujuan tersebut, kegiatan ini memberikan beberapa manfaat, antara lain sebagai alternatif kegiatan yang menarik bagi anak-anak agar tidak selalu bermain gawai, memberikan pengalaman anak-anak dalam hal problem solving, kreativitas, dan kolaborasi, dan memberikan pengalaman lapangan kepada mahasiswa Polibatam dengan mengaplikasikan ilmu yang diperoleh melalui  Problem Based Learning (PBL) untuk kepentingan masyarakat. Workshop ini dilakukan dalam empat pertemuan (Maret hingga April 2021), bertempat di gedung Taman Pendidikan Al Qur’an (TPA) At Taqwa, perumahan Graha Nusa Batam. Robot yang digunakan adalah line follower analog. Pre-test dan post-test dilakukan untuk mengukur peningkatan pemahaman peserta terhadap materi. Hasil tes ini menunjukkan bahwa melalui workshop ini pemahaman peserta mengenai teknologi robotika dapat meningkat hingga 25% untuk peserta berusia 8-11 tahun dan 22% untuk peserta berusia 12-15 tahu