Real-Time Traffic Sign Recognition System based on Colour Image Segmentation

Patnaik, Girish Kumar; Patil, Manoj E.

doi:10.5120/14430-2575

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Region growing is a pixel-based image segmentation method that starts by selecting a starting point or seed pixel. Then, the region develops by adding neighboring pixels that are uniform, according to a certain match criterion, increasing step-by-step the size of the region [46]. This method was used by Nicchiotti et al [47] and Priese et al [48] for TSDR.…”

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

129

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The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

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Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

129

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“…Detecting small or distant objects in the high-resolution scene photographs from the car is necessary to deploy self-driving cars safely. Many objects, such as traffic signs [2,3] or pedestrians [4], are often barely visible on the high-resolution images. In medical imaging, early detection of masses and tumors is crucial for making an accurate, early diagnosis, when such elements can easily be only a few pixels in size [5,6].…”

Section: Min Rectangle Area Max Rectangle Areamentioning

confidence: 99%

Augmentation for small object detection

Kisantal¹,

Wojna²,

Murawski³

et al. 2019

9th International Conference on Advances in Computing and Information Technology (ACITY 2019)

471

104

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In the recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap between small ground-truth objects and the predicted anchors is much lower than the expected IoU threshold. We conjecture this is due to two factors; (1) only a few images are containing small objects, and (2) small objects do not appear enough even within each image containing them. We thus propose to oversample those images with small objects and augment each of those images by copy-pasting small objects many times. It allows us to trade off the quality of the detector on large objects with that on small objects. We evaluate different pasting augmentation strategies, and ultimately, we achieve 9.7% relative improvement on the instance segmentation and 7.1% on the object detection of small objects, compared to the current state of the art method on MS COCO.

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“…It can be quite computationally expensive, and by nature, sensitive to noise. Many shape detectors are slow in computing over large and complex images [10] which will become an issue in a real-time system. The complexity of color based lies in the usage of three intensity values depending on the color space instead of just working in gray level image as in shape based.…”

Section: Introductionmentioning

confidence: 99%

“…Color is segmented using K-means clustering and effective robust kernel based Fuzzy C-means clustering resulting to a more accurate segmentation but has increased computational complexity. In [10], color segmentation in RGB color space followed by shape segmentation using joint transform correlator (JTC) template matching had been implemented for a more efficient system. A study [12] solved the unreliability issue of RGB due to sensitivity to illumination variation by working on HSV color space along with shape based filtering through template matching.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

Santos¹,

Abu²,

Oppus³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Road traffic accidents are primarily caused by drivers error. Safer roads infrastructure and facilities like traffic signs and signals are built to aid drivers on the road. But several factors affect the awareness of drivers to traffic signs including visual complexity, environmental condition, and poor drivers education. This led to the development of different ADAs like TSDR that enhances vehicle system. More complex algorithms are implemented for improvement but this affects the performance of a real-time system. This study implements a real-time traffic sign detection and recognition system with voice alert using Python. It aims to establish the proper trade-off between accuracy and speed in the design of the system. Four pre-processing and object detection methods in different color spaces are evaluated for efficient, accurate, and fast segmentation of the region of interest. In the recognition phase, ten classification algorithms are implemented and evaluated to determine which will provide the best performance in both accuracy and processing speed for traffic sign recognition. This study has determined that Shadow and Highlight Invariant Method for the pre-processing and color segmentation stage provided the best trade-off between detection success rate (77.05%) and processing speed (31.2ms). Convolutional Neural Network for the recognition stage not only provided the best trade-off between classification accuracy (92.97%) and processing speed (7.81ms) but also has the best performance even with lesser number of training data. Embedded system implementation utilized Nvidia Jetson Nano with interface Waveshare IMX219-77 camera, Nvidia 7" LCD and generic speaker and programmed in Python with OpenCV, sci-kit learn and Pytorch libraries. It is capable of running at an adaptive frame rate from 8-12 frames per second with no detection and down to approximately 1 frame per second when there is traffic sign detected.

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Real-Time Traffic Sign Recognition System based on Colour Image Segmentation

Cited by 11 publications

References 24 publications

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Augmentation for small object detection

Real-Time Traffic Sign Detection and Recognition System for Assistive Driving

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