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... process of image processing can be monitored by computer through the same network with raspberry pi. Connection between hardware on the prototype based on their work system can be seen in Fig 3. Robot boat prototype is using lippo 3 cell battery as power source with capacity 3000 mAh. The power source of Raspberry Pi is ultimate battery eliminator circuit (ubec) which convert 12 V from the battery to 5 V that is used as Raspberry Pi power source. ...
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Citations
... Some of the previous research in Computer Vision tasks for Raspberry Pi has been conducted. Research on using the Raspberry Pi 3 as an Object Detector on a Robot Boat was carried out and showed quite good responsiveness on boat devices [2]. In a study by Rosa Andrie et al. [3], an experiment was carried out to predict traffic density using a Raspberry Pi and added parallel computing capabilities using Intel NCS2. ...
This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.
... Untuk mendapatkan hasil color filter HSV ini melalui beberapa tahapan. Hue mengindikasikan panjang gelombang warna utama dan memiliki rentang antara 0-255[18]. Saturasi merujuk pada intensitas kecerahan warna, yang ditingkatkan berdasarkan nilai saturasi murni dalam warna akhir. Value mengukur tingkat kecerahan warna, di mana 100% mewakili kecerahan maksimal dan 0% menghasilkan kegelapan penuh pada warna tersebut. ...
Dalamperkembangan teknologisaatini,khususnya disektorindustri,robotdengankecerdasanbuatanmengemban peranpentingdalammeningkatkan efisiensiwaktukerjamanusia.PenelitianinimenciptakansebuahrobotAGVyang dilengkapisistemkecerdasanbuatan,menggunakan kamerasebagaisensorvisualuntukmendeteksi garislintasan sebagaipanduannavigasi.RobotAGVinimemanfaatkanOpenCVuntukpemrosesancitradenganmetodefilterwarna HSV.Metodeinimencakup teknikmorfologi danGaussianbluruntukmengenali garislintasanyangakandilalui. Setelah prosesidentifikasijalur, robot AGV akan bergerak sesuai jalur yang terdeteksioleh kamera.Pengujian perangkatkeras dilakukandi laboratorium,bahwamodenavigasirobotAGV berdasarkanmetode HSV mampu berfungsibaikdanmenghasilkantingkatakurasideteksijaluryangtinggi.
... So that, when the color value changes continuously, the RGB value cannot directly reflect this change. Another widely used color space is HSV [17], where H and S represent hue and saturation respectively, which are often used for segmentation and detection. However, HSV is still a high dimensional color feature, which leads to an increasement of computational complexity on the one hand, and more seriously, due to image noise, it is easy to cause points to be misclassified in the image sequence. ...
... Then for the movement of the self-driving car, it uses a DC motor as a steering and driver. Raspberry will be connected serially with Arduino for image processing and drive interconnection [14], [15]. In this study, we developed a self-driving car in real time [16] library [14], [15]. ...
... Raspberry will be connected serially with Arduino for image processing and drive interconnection [14], [15]. In this study, we developed a self-driving car in real time [16] library [14], [15]. It will be combined according to the purpose of making this AGV which makes it easier to detect the location of the AGV [16]. ...
The development of technology in the transportation sector at this time is increasingly crucial. So the company innovates to create a car that can run itself with a high level of security. In this study, we designed an autonomous drive system for a 1:10 scale RC car using the main components in the form of a Raspberry Pi 4 and a Raspberry Pi camera as image processing for automatic control of an self driving car. Then the Arduino Nano, BTS7960, and Driver L298N components are used to regulate the movement of the DC motor. In this article, the control strategy of this self-driving car will be shown which will be implemented to detect lanes as a guide to walk autonomously. This study uses the HSV color filer method with morphology techniques to detect the path to be passed. This study resulted in a path detection that was very accurate and operated in real-time when compared to the CNN method using sampling paths to be passed that had previously been researched. After the path is detected, the interconnection between the mini computer and the microcontroller will work to synchronize the path detection and motor movement. In trials and hardware implementations carried out in the self-driving car laboratory with artificial intelligence, it can work according to the algorithm created with a success rate of 90%.
... Jika nilai saturation kurang akan menunjukan warna abu-abu. Value mencakup nilai 0 sampai 255, dengan 0 menjadi warna gelap dan nilai 255 menjadi warna cerah [17]. ...
Perkembangan teknologi di sektor transportasi di masa ini semakin krusial. Sehingga perusahaan berinovasi menciptakan mobil yang dapat berjalan sendiri dengan tingkat keamanan yang tinggi. Pada penelitian ini, kami merancang sistem self-driving untuk mobil RC skala 1:10 menggunakan komponen utama berupa Raspberry Pi 4 sebagai pengolahan citra untuk kendali otomatis pada autonomous car . Untuk mengatur pergerakan roda belakang dan steering menggunakan motor DC. Penelitian ini menerapkan computer vision yang dipakai untuk sistem navigasi agar dapat berjalan sesuai dengan lintasan. Permasalahan yang dijumpai pada penelitian sebelumnya adalah masih mengambil sampel lintasan terlebih dahulu yang dirasa kurang efisien karena pada jalan yang belum diambil sampelnya tidak dapat dilalui robot tersebut. Untuk memecahkan permasalahan ini maka peneliti menerapkan algoritma HSV agar dapat mengikuti lintasan secara real-time. Algoritma HSV(hue, saturation, value) merupakan sistem untuk mendeteksi tepi garis lintasan dengan memproses gambar dari kamera Raspberry Pi . Dari hasil kalibrasi nilai threshold yang digunakan adalah sebesar Hmin = 135 dan Hmax = 179, Smin = 70 dan Smax = 255, dan nilai V sebesar Vmin = 53 dan Vmax = 106 agar dapat mendeteksi jalur lintasan secara jelas, baik di dalam ruangan maupun diluar ruangan, dan HSV toleran terhadap perubahan intensitas cahaya. Itulah keuntungan dari algoritma HSV. Berdasarkan hasil pengujian dan implementasi robot ini dengan menggunakan kecerdasan buatan dapat bekerja sesuai dengan algoritma yang sudah dibuat dengan tingkat akurasi deteksi jalur yang cukup tinggi.
... HSV functions in colour selection because the HSV method approaches the visual way of the human eye in distinguishing colours. HSV is the result of the transformation of the RGB colour form (Red, Green, Blue), and the light intensity or colour brightness value varies from 0 to 100 [16]. ...
With the development of technology, especially in the field of robotics, daily human activities can be carried out with artificial intelligence. One of the artificial intelligence technologies that help ease the burden on humans, especially in terms of driving, is self-driving cars. In this case, self-driving cars have several methods with GPS systems, radar, lidar, or cameras. In this study, a self-driving car system was designed on a navigation path model using a street mark detector with an intermediary sensor, namely a camera as a vision sensor. This self-driving car system uses a prototype called an autonomous car to walk on a path which is a self-driving car navigation direction based on the detected line to be able to detect camera sensors that process line images from the camera using HSV. method. In this study, a self-driving car system has been successfully designed using a microcontroller, namely Raspberry Pi 4 as a programmer and L298n motor driver, BTS7960 as a driver for a self-driving car. The Raspberry Pi 4 sends real-time images through the camera as a vision sensor which then detects a line to navigate the movement of this self-driving car. By using image processing, the resulting level of precision can reach the average value according to the direction of the self-driving car.
... So that, when the color value changes continuously, the RGB value cannot directly reflect this change. Another widely used color space is HSV [17], where H and S represent hue and saturation respectively, which are often used for segmentation and detection. However, HSV is still a high dimensional color feature, which leads to an increasement of computational complexity on the one hand, and more seriously, due to image noise, it is easy to cause points to be misclassified in the image sequence. ...
... And it is not intuitive to continuously change of color. HSV color space is widely used in image processing [22,23,24]. H and S are usually be used to segment and detect images. ...
MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking performance will be deteriorated. In this paper, we address the challenges above-mentioned by developing a tracking method that combines the background models and the graded features of color-names under the MeanShift framework. This method significantly improve performance in the above scenarios. In addition, it facilitates the balance between detection accuracy and detection speed. Experimental results demonstrate the validation of the proposed method.
... In brief, HSV is a well-known algorithm that can detect and localize the target in pictures even with a low-cost camera presented with similar colours and robust to noises [20]. The input image from red, green, and blue (RGB) space is initially converted to HSV space into separate objects with different colours to use HSV. ...
The history recorded moon as the most inspiring object in the sky, but it
combined with visibility issues to study the phases. This research paper
proposes a novel algorithm named Sunfa Ata Zuyan (SAZ), which is meant
to extend the shape detection algorithms to aim for lunar phase deceleration
and overcome the difficulties encountered by the previous methods to find
the moon and determine its phase. The paper sets to investigate two aims.
First, propose the add-on algorithm SAZ to determine the lunar phase's data
faster. Secondly, evaluate the Raspberry Pi as the main CPU due to its
compact size and power as the primary processor based on the idea of a
portable designed system. Then to examine the ability of the SAZ algorithm,
it's combined with famous algorithms like hue, saturation and value (HSV),
Canny, erosion, shape detection, and binarization has been tested on both
personal computers (PC) and Raspberry Pi with the same images being
compared. The results show that SAZ will help the shape detection
algorithm to find the object and disclose the moon phases. Furthermore, the
Raspberry Pi, functioning as a CPU, can perform as a hand-to-hand system
to determine the lunar phase as a compact portable remote sensing structure.
