Improved U-net for Zebra-crossing Image Segmentation

Zhong, Jiahao; Feng, Wei; Lei, Qujiang; Le, Shangzhi; Wei, Xiangying; Wang, Yuhe

doi:10.1109/iccc51575.2020.9345244

Cited by 4 publications

(2 citation statements)

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“…In [57], the U-Net model was proposed for pedestrian crossing segmentation in images. A dataset of 150 training images and 30 validation images was created.…”

Section: Related Workmentioning

confidence: 99%

Detecting Wear and Tear in Pedestrian Crossings Using Computer Vision Techniques: Approaches, Challenges, and Opportunities

Rosa,

Afonso,

Gaspar

et al. 2024

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Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings.

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“…In [57], the U-Net model was proposed for pedestrian crossing segmentation in images. A dataset of 150 training images and 30 validation images was created.…”

Section: Related Workmentioning

confidence: 99%

Detecting Wear and Tear in Pedestrian Crossings Using Computer Vision Techniques: Approaches, Challenges, and Opportunities

Rosa,

Afonso,

Gaspar

et al. 2024

Information

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“…II. LITERATURE REVIEW Zebra-crossings are detected by looking for groups of concurrent lines [1] where three methods are used for color detection and segmentation which includes RGB images being converted into IHLS color space and these methods are tested on outdoor images [2] and many other threshold image techniques such as Gaussian filter, Canny edge detection Contour, and Fit Ellipse [3][4] are used for traffic sign recognition with Kalman filter [5] which also includes Block-based Hough proposed by Yu-Quin Bao [6] and transform and directional variance techniques [7], a novel approach to detect and locate the zebra-crossings and the system is found out to be feasible for use on public roads around the world [8] to obtain 13,40 high-quality photo-realistic images from the video from 13 classes of various objects [9]. The design of a low-power, low-latency electronic mobility assistance for blind persons revealed that decision trees, random forests, and KNNs may all be used to recognise objects [10].…”

Section: Introductionmentioning

confidence: 99%

Zebra Crossing Detection Using Concatenation of SIFT-Gabor Features

Madake

2023

IJRASET

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Zebra-crossing recognition is a challenging activity for visually impaired navigating safely. This paper proposes a zebra-crossing scene recognition model-based fusion of texture features and geometrical features. The Gabor features for 8 orientations are extracted for different orientations Zebra Crossing lines. The SIFT is employed for keypoint detection and description. The final features vector is obtained by concatenating Gabor and SIFT features. The feature vector is optimized using K-means clustering and Principle Component Analysis (PCA). The optimized feature vectors were classified using five Classifiers, SVM, Random Forest, Decision Tree, Naïve Bayes Classifiers. The proposed model successfully recognizes the crosswalks with 89% accuracy. The different classifier models are evaluated for Precision, Accuracy, F1 Score, Recall, and RoC. This paper specifies the classification of two class positive which is zebra-crossing and negative which is no zebra-crossing. The designed system will classify either the crosswalk is detected or not on the basis of percentage specified for each class. This can be done by matching the features of the trained dataset. Those features are matched with the real videos or images to recognize the motion and classify into specific class. More specific crosswalk detection in real time can be executed to get more specific type of positive and negative class and classification.

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