Pendeteksian Ruang Kosong Parkir di dalam Ruangan

2018 ◽  
Vol 10 (1) ◽  
pp. 34-40
Author(s):  
Nunik Afriliana ◽  
Rosalina Rosalina ◽  
Regina Valeria
Keyword(s):  
Real Time ◽  
Line Detection ◽  
Cascade Classifier ◽  
Index Terms

Menemukan tempat parkir kosong di tempat parkir dalam ruangan seperti pusat perbelanjaan menjadi kesulitan banyak pengemudi, terutama saat jam sibuk di kota-kota besar. Dalam makalah ini, sebuah sistem deteksi kekosongan tempat parkir dalam ruangan diusulkan, dengan menggunakan sistem kamera yang melibatkan OpenCV untuk mempercepat waktu dalam mencari tempat parkir bagi pengemudi kendaraan dengan memberi mereka informasi lokasi dan tempat parkir. Sistem ini menggunakan metode deteksi objek statis, yaitu Haar-Like Cascade Classifier yang dikombinasikan dengan Hough Line Detection untuk mengidentifikasi area parkir kosong dari gambar parkir yang diambil secara real time melalui kamera IP atau kamera USB. Sistem ini dirancang untuk disematkan dengan sistem manajemen parkir sebuah bangunan sebagai alat yang menyediakan tempat parkir untuk membantu pengemudi kendaraan memasuki area parkir. Index Terms—Haar-Like Cascade Classifier, Hough Line Detection, Sistem Maanajemen Parkir

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot

Model Klasifikasi Mata Katarak dan Normal Menggunakan Histogram

2017 ◽  
Vol 9 (1) ◽  
pp. 33-36
Author(s):  
Valencia Wirawan ◽  
Yustinus Eko Soelistio
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Index Terms

Telah banyak penelitian pada citra medis telah diadopsi oleh sebagian besar ilmuwan dan dokter yang dapat membantu dalam mendeteksi gangguan pada mata terutama katarak. Namun, umumnya penelitian tersebut menggunakan citra medis atau digital yang relatif mahal dan sulit didapatkan oleh sebagian orang, dan metode yang rentan akan translasi (pergeseran), serta perubahan ukuran gambar dan bentuk objek. Penelitian ini mengembangkan sebuah metode menggunakan model histogram untuk mengklasifikasi mata katarak dari citra digital dengan (1) format yang lebih umum seperti JPEG dan (2) lebih toleranterhadap translasi dan perubahan ukuran. Metode ini juga mampu bekerja dengan baik menggunakan citra digital dalam citra mata yang tidak tegak lurus terhadap kamera. Metode ini mencapai akurasi 79,03% dalam kondisi bebas dan 88.47% dalam kondisi mata tegak lurus terhadap kamera. Metode ini mempunyai kompleksitas yang rendah sehingga dapat digunakan pada komputer dengan spesifikasi rendah dan sistem yang membutuhkan kecepatan mendekati real-time. Index Terms—Image processing, cataract, classification, histogram

Real-time optical line detection

1987 ◽  
Vol 63 (4) ◽  
pp. 230-232 ◽  
Author(s):  
George Eichmann ◽  
Yao Li
Keyword(s):  
Real Time ◽  
Line Detection ◽  
Optical Line

A Real-Time Sea-Sky-Line Detection Method under Complicated Sea-Sky Background

2012 ◽  
Vol 182-183 ◽  
pp. 1826-1831 ◽  
Author(s):  
Yang Gui ◽  
Xiao Hu Zhang ◽  
Yang Shang ◽  
Kun Peng Wang
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Computational Cost ◽  
Input Image ◽  
Line Detection ◽  
Image Position ◽  
Interior Points ◽  
Line Fitting ◽  
Ransac Algorithm ◽  
Correlation Matching

A real-time sea-sky-line detection method under complicated sea-sky background is presented. Firstly, a black-white template is constructed and used for fast correlation matching in several searching regions which are predefined in input image, position of maximal correlation coefficient in each predefined region is hunt out, and coordinates of several candidate sea-sky-line points are made certain according to the position. Then, RANSAC algorithm is applied to preserve interior points which are really on the sea-sky-line and eliminate exterior points which are not. Finally, line parameters of the sea-sky-line can be gained by applying least squares line fitting for all interior points. The pixels of several regions in the image instead of the whole image need to be considered, so computational cost can be reduced dramatically. The experimental results show that the proposed method can detect out sea-sky-line under complicated sea-sky background effectively and has many advantages such as strong robustness and speedy calculation.

scholarly journals Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2038
Author(s):  
Zhen Tao ◽  
Shiwei Ren ◽  
Yueting Shi ◽  
Xiaohua Wang ◽  
Weijiang Wang
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Social Progress ◽  
Line Detection ◽  
Detection Accuracy ◽  
Railway Transportation ◽  
Deep Convolutional Neural Networks ◽  
Detection Algorithms ◽  
Rail Line ◽  
Binary Segmentation

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.

Real-time vehicle detection with foreground-based cascade classifier

2016 ◽  
Vol 10 (4) ◽  
pp. 289-296 ◽  
Author(s):  
Xiaobin Zhuang ◽  
Qiuxia Wu ◽  
Wenxiong Kang
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Cascade Classifier
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