A Convolutional Neural Network Based Deep Learning Technique for Identifying Road Attributes

Jan, Zohaib; Verma, Brijesh; Affum, J K; Atabak, Sam; Moir, Lachlan

doi:10.1109/ivcnz.2018.8634743

Cited by 17 publications

(21 citation statements)

References 25 publications

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“…These road safety attributes include various traffic signs [1]- [3], roads [5], pedestrians [6], vehicles [7], cyclists [8], and road markings [9]. In [10], a Convolutional Neural Network (CNN) was trained to identify several AusRAP attributes namely, speed signs; sign 60, 100, and 110, lane lines, poles, trees, barriers, rumble strips and roads and the distance was calculated between them. Attributes such as road, lane line, rumble strip, barrier, 110 sign and 100 sign obtained better results in terms of object detection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fully Convolutional Network for Road Safety Attribute Detection

Sanjeewani

Verma

2021

IEEE Access

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Even though, deep learning methods demonstrate an outstanding performance in various applications, success of deep learning depends upon appropriately setting of its parameters in achieving most accurate results. Therefore, in this paper, a novel Particle Swarm Optimization (PSO) based technique is introduced to optimize a Fully Convolutional Network (FCN) to recognize road safety attributes. The proposed technique optimizes parameters such as number of convolution layers, activation function, pooling type, attribute image size, number of iterations and learning algorithms. The proposed technique has been evaluated using a custom dataset prepared by extracting images from video data provided by our industry partner. The evaluation results reveal that the proposed technique can automatically determine the most suitable combination of parameters corresponding the highest classification accuracy for road safety attributes by exploring the solution space. Extensive tests in conjunction with the statistical experiments convinced that proposed approach is an appropriate technique for automatically optimizing FCN's parameters. The best sets of parameters were obtained by the proposed technique for different road safety attributes with a classification accuracy of 92-100%.

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Section: Introductionmentioning

confidence: 99%

Optimization of Fully Convolutional Network for Road Safety Attribute Detection

Sanjeewani

Verma

2021

IEEE Access

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“…age or size of the vehicle) [2]. Over the last decades, remarkable practical and methodological developments have been achieved for decreasing dangers and accident rates on roads based on heterogeneous technologies such as stochastic, heuristic and fuzzy [3]- [6]. However, the lack of knowledge in terms of human factors, driving issues, vehicle mechanisms are the main problem for solving human daily traveling accidents as a whole [7].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

2020

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Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

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“…DTMR collects data from the Queensland road network to assess the road safety conditions, improve the road infrastructure and reduce fatalities in road accidents [4], [5]. The data collection is performed annually for Mobile Laser Scanning (MLS) data and Digital Video Recording (DVR) data.…”

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confidence: 99%

Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

Perera

Verma

2022

IEEE Access

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Some roadside objects pose a significant danger to pedestrians and vehicles when they are too close to the road. A few examples are trees, poles and fences. Their proximity to the road can change over time due to natural conditions or human activities. Early detection of severe roadside conditions can help avoid accidents and save lives. However, detecting the roadside severity objects requires many resources and new techniques due to the size and complexity of the road network. Deep learning and image processing techniques can be leveraged to address this requirement and build an automatic roadside severity detection system. In this work, we propose a novel roadside attribute and distance calculation technique that extends our previous work in this area (lane-line method). The past work depended on the detected lane-line widths to calculate the distances. This method made mistakes in the presence of challenging road conditions and misclassifications. Here, we propose to combine camera configuration data with a neural network detector to develop a distance vs pixel model for reliable road severity distance calculation. We use camera metadata to transform the 2D image data predicted by the deep neural network to a 3D space. The improved model was tested with a real-world dataset. Compared to the lane-line method, the new combined model reported 36% and 37.5% accuracy improvements in right and left-hand side distances, respectively.INDEX TERMS deep learning, fully convolutional neural networks, road safety attributes I. INTRODUCTION

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A Convolutional Neural Network Based Deep Learning Technique for Identifying Road Attributes

Cited by 17 publications

References 25 publications

Optimization of Fully Convolutional Network for Road Safety Attribute Detection

Optimization of Fully Convolutional Network for Road Safety Attribute Detection

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

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