Usage of convolutional neural network ensemble for traffic sign recognition

Предлагается для распознавания дорожных знаков использовать ансамбль сверточных нейронных сетей, который является модификацией робастного метода распознавания на основе нейронных сетей глубокого обучения. Данный ансамбль повышает скорость работы робастного метода распознавания, а также позволяет увеличить быстродействие с сохранением высокой точности распознавания за счет удаления из набора данных значений, которые не представляют полезной нагрузки

PERBAIKAN DISTORSI, DETEKSI DAN SEGMENTASI RAMBU LALU LINTAS PADA REKAMAN DASHCAM MOBIL

Rambu lalu lintas merupakan bagian perlengkapan jalan berupa lambang, huruf, angka, kalimat, dan/atau perpaduan yang berfungsi sebagai peringatan, larangan, perintah, atau petunjuk bagi pengguna jalan. Hadirnya sistem deteksi dan segmentasi rambu lalu lintas akan membantu pengguna jalan terhindar dari pelanggaran lalu lintas dan kecelakaan akibat melalaikan keberadaan rambu, dan tidak membahayakan pengguna jalan lain. Penelitian mengajukan sistem deteksi dan segmentasi rambu lalu lintas dengan input video rekaman dashcam pada kendaraan mobil, mengombinasikan segmentasi berbasis warna dengan range threshold color space HSV, operasi morfologi, dan segmentasi berbasis bentuk dengan fitur metric dan eccentricity. Berbeda dengan penelitian terdahulu yang menggunakan kamera biasa / handphone; penelitian menggunakan dashcam sebagai kamera video yang secara spesifik dibuat untuk merekam tampilan jalan selama kendaraan beroperasi. Distorsi hasil rekaman dashcam diperbaiki dengan aplikasi Camera Calibrator pada Matlab, menggunakan pola kalibrasi checkerboard. Sistem yang diajukan mampu memperbaiki distorsi, melakukan deteksi dan segmentasi rambu , serta memunculkan notasi jenis rambu dengan tingkat akurasi 91.67%, selama rambu berada pada range 1.5 hingga 15 meter dari kendaraan mobil dan tidak ada objek lain yang menutupi rambu, dengan rambu larangan sebagai rambu yang lebih sulit terdeteksi karena memiliki porsi warna dasar yang lebih sedikit, hanya sebagai garis tepi

Detection and recognition of speed limit road signs

Diplomová práce se zabývá návrhem a implementací systému pro detekci a rozpoznání omezení rychlosti z dopravních značek. Konkrétně jde o rozpoznání červených kruhových značek Nejvyšší povolená rychlost z obrazových dat za použití metod počítačového vidění. V rámci práce je naprogramováno a otestováno několik metod. Ve finálním řešení je použita segmentace pomocí YCbCr barevného modelu. Detekce kruhové značky i závěrečná klasifikace je vyřešena porovnáním se vzorem. Pro rozpoznání v reálném čase je použit algoritmus sledování detekovaných značek mezi snímky videosekvence. Program je vytvořen v prostředí MATLAB a Simulink. Výsledkem je prototyp jednoduchého asistenčního systému, který je možné implementovat na jakýkoli počítač s kamerou. Správná funkce algoritmu byla prokázána při testech v reálném provozu.This master‘s thesis describes the design and implementation of the system for detection and recognition of speed limit road signs. It focuses on the recognition of the red circular speed limit sign from the image data using the computer vision methods. Several methods were programmed and tested as a part of this thesis. In the final solution, the segmentation based on YCbCr color model is used. Detection of the circular sign and final classification is performed by template matching method. Algorithm for the tracking of the detected signs between frames of the video is used for better performance in real-time recognition. Application is developed using MATLAB and Simulink. The result is a simple driver assistance system prototype, which can be implemented in any computer with camera. The correct function of the algorithm was confirmed during a testing in a traffic.

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset