Unstructured Road Detection and Steering Assist Based on HSV Color Space Segmentation for Autonomous Car

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽

10.3390/electronics10141665 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1665

Author(s):

Jakub Suder ◽

Kacper Podbucki ◽

Tomasz Marciniak ◽

Adam Dąbrowski

Keyword(s):

Edge Detection ◽

Optimal Path ◽

Color Space ◽

Low Complexity ◽

Detection Algorithm ◽

Lane Detection ◽

Detection Methods ◽

Raspberry Pi ◽

Line Detection ◽

Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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An efficient image retrieval based on HSV color space

2011 International Conference on Electrical and Control Engineering ◽

10.1109/iceceng.2011.6058026 ◽

2011 ◽

Cited By ~ 19

Author(s):

Ching-Hung Su ◽

Huang-Sen Chiu ◽

Tsai-Ming Hsieh

Keyword(s):

Image Retrieval ◽

Color Space ◽

Hsv Color Space

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Image Deblurring Based on Fuzzy Kernel Estimation in HSV Color Space

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-27541-9_48 ◽

2019 ◽

pp. 590-603

Author(s):

Aidi Zhao ◽

Jianhua Zhang ◽

Xiaoling Lv ◽

Minglu Zhang

Keyword(s):

Color Space ◽

Kernel Estimation ◽

Image Deblurring ◽

Hsv Color Space

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Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021854719 ◽

2021 ◽

Vol 8 (5) ◽

pp. 929

Author(s):

Hurriyatul Fitriyah ◽

Rizal Maulana

Keyword(s):

Learning Algorithm ◽

Color Space ◽

Outdoor Environment ◽

Training Data ◽

Distance Transform ◽

Validation Data ◽

Hsv Color Space ◽

Testing Data ◽

Artificial Neural Network Ann ◽

Center Distance

Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah Rectangularity, Edge-to-Center distances function, dan Distance Transform function. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara offline. Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%. AbstractWeed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15% as validation data, and 15% as testing data).

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Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana

Jurnal Teknik Elektro ◽

10.15294/jte.v12i2.26676 ◽

2020 ◽

Vol 12 (2) ◽

pp. 72-79

Author(s):

Ismawan Noor Ikhsan ◽

Son Ali Akbar

Keyword(s):

Machine Vision ◽

Color Space ◽

Region Of Interest ◽

Contour Detection ◽

Segmentation Method ◽

Smart System ◽

Hsv Color Space ◽

Aerial Vehicle ◽

Detection Evaluation ◽

Flying Robots

Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.

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