obstacle and road sign detection
11846285
An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System
Road obstacles cause serious accidents that have a severe impact on driver safety, traffic flow efficiency and damage of the vehicle. Detecting obstacles are important to prevent or to reduce such kind of the accidents and fatalities. However, it is difficult and becomes tricky because of some problems like presence of shadow, environmental changes or a sudden action of any moving things (e.g. car overtaking, animal coming) and many more. Thereby, this paper aims to design an obstacle detection technique based on (i) moving cameras and (ii) moving objects. These methods are applied to obstacle detection phase, in order to identify the different obstacles (e.g. potholes, animals, stop sign, obstacles, bumps, road cracks) by considering road dimensions. A new technique is introduced for detecting obstacles from moving camera and moving objects which overcomes several limitations over stationary cameras and moving/stationary objects. Further, paper reviews recent research trends to detect obstacles for moving cameras and moving objects with discussion of key points and limitations of each approach. Finally, the results show that the proposed method is more robust and reliable than the previous approaches based on the stationary cameras.
Vidhi R Shah Sejal V Maru Rutvij H Jhaveri
17840216
Road traffic sign detection and classification
A vision based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown.
IEEE Trans. Ind. Electron.
Arturo de la Escalera Luis E Moreno Miguel Angel Salichs Jose Maria Armingol
57071988
Applied Image Processing and Systems for Welfare. Development of the Robotic Travel Aid for the Visually Impaired.
The Robotic Travel Aid (RoTA) is a motorized wheelchair equipped with vision, sonar, and tactile sensors, as well as a map database system. The visually impaired can get mobility, orientation, and obstacle information from the RoTA, and they can inquire about their present location, landmarks, and future parts of their route. The RoTA can help the visually impaired avoid vehicles along a road with lane marks or along a sidewalk marked with braille. In this paper, we propose the locomotion strategy of our robot, which is based on "sign pattern based stereotyped action." This paper describes 1) danger estimation, 2) pedestrian detection, and 3) the map system for the RoTA. Experimental results showed the effectiveness of our proposed method. Furthermore, a psychologist wearing an eye mask evaluated a RoTA (not the vision based type but the sonar based type) and the experimental results showed the RoTA to be a very valuable aid to the visually impaired.
Shinji Kotani Noriaki Kiyohiro Hideo Mori
220335979
Object Detection using IoT and Machine Learning to Avoid Accident and Improve Road Safety
At present situation the human beings are going through many accidents during the road way transportation. Simultaneously they lose their life and significant properties in those accidents. Most of the Indian roads in rural and suburban's are not ideal for driving due to faded lanes, irregular potholes, inappropriate and unseen road signs, which caused many accidents, lost lives and caused serious damage to vehicles. The most difficult task is to detect obstacles on the highway. The basic concept is to design a system that has the effect of detecting the presence of an obstacle in the track of the vehicles. In the proposed work, the Raspberry Pi Camera module is used for object detection and image acquisition. A thorough study is performed on a test image to test the best algorithm suitable for detecting image boundaries. The framework performs preprocessing utilizing the Mean Subtracted Difference Enhancement (MSDE) strategy and afterward segmentation is performed. The classification is done by using proposed Advanced Classifier for the detection of objects. The system can classify objects like vehicles, animals, humans, etc. Once the object is detected the system informs the user to slow down the vehicle through a voice message. A sufficient analysis is carried out to consolidate the results obtained. The result analysis shows that proposed system is more precise and consumes less time than existing system. Keywords Raspberry Pi, Object detection, Image processing, IoT, Segmentation
Shrinath Oza Sunil D Rathod
196110910
Internet of Things in Vehicle Safety Obstacle Detection and Alert System
Recent development of the Internet of Things (IoT) and Smart Things provides an opportunity for the informationization for the automotive industry. This article describes Obstacle Detection and Alert System (ODAS) both incorporated as a single system for the obstacles such as speed breakers, barricades on the road using Internet of Things. Obstacle detection system uses in built algorithm to detect an obstacle on the road using minimal vehicle parameters such as vehicle speed, steering angle. Obstacles locations thus marked by the detection system are stored locally and uploaded to cloud from time to time. Cloud server processes data from different vehicles and finalizes that there is a real obstacle at the particular location based on its own algorithm. Obstacles locations thus confirmed by the cloud server are downloaded to obstacle alert system from time to time, which alerts the driver about the obstacle on the road when the driver is nearing the location of the obstacle. This system also provides option to see obstacles location super imposed on google maps so that user can plan his efficient route in advance. Existing systems only have road sign indications for obstacles such as speed breakers, barricades which the driver might not notice during driving, especially at night times. Existing google maps data do not have option to find road obstacles such as speed breakers, barricades information on it. The system aims to overcome these short comings by a dedicated system which provides audible and visual alert about road obstacles.
D Umakirthika P Pushparani M Rajkumar
139107788
Detecting inter sectional accuracy differences in driver drowsiness detection algorithms
Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4% In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark skinned drivers faces. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. Results presented here show that models trained using publicly available datasets suffer extensively from over fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.
2020 International SAUPEC/RobMech/PRASA Conference
Mkhuseli Ngxande Jules-Raymond Tapamo Michael Burke
28941195
Detection and classification of road signs for automatic inventory systems using computer vision
This article describes an intelligent system that enables the automatic recognition of road signs from image sequences in road environments. The main difficulties the system has to deal with are related to changes in lighting conditions, obstacles blocking the view, the presence of objects with geometric and chromatic similarities and the absence of previous knowledge about their position and orientation. The application of different techniques allows the system to overcome this variety of problems. Therefore, the road sign recognition system is based on a first pre processing of images, making use of information about road geometry to isolate those areas of the image where road signs may appear. The detection step uses colour and shape analysis to determine the regions of the image where potential road signs may be located. A third step focuses on recognition and classification using pattern matching and edge feature analysis. The proposed algorithm has been tested in different weather and lighting conditions and roads one and two lane roads and motorways, overall, a total of 1200 kilometres with a very high success rate of detection and classification as the experimental results show.
Integr. Comput. Aided Eng.
Gustavo A Pelaez Coronado Maria Romero Munoz Jose Maria Armingol Arturo de la Escalera Juan Jesus Munoz Wouter van Bijsterveld Juan Antonio Bolano
211228102
On the Performance of Extended Real Time Object Detection and Attribute Estimation within Urban Scene Understanding
Whilst real time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles) and road sign age within the scene. By contrast, for an autonomous vehicle to be truly able to interact with occupants and other road users using a common semantic understanding of the environment it is traversing it requires a considerably extended scene understanding capability. In this work, we consider the performance of extended "long list" object detection, via an extended end to end Region based Convolutional Neural Network (R CNN) architecture, over a large scale 31 class detection problem of urban scene objects with integrated object attribute estimation for appropriate colour and primary orientation. We examine the extended performance of this multiple class object detection and attribute estimation task operating in real time with on vehicle processing at 10 fps. Our work is evaluated under a range of real world automotive conditions across multiple complex and cluttered urban environments.
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Khalid N Ismail T Breckon
219468168
Detection of level crossing barriers using the histogram of oriented gradients method and support vector machine
Railroad crossing is a place where the railroad lines intersect with other roads, such as a highway. Referring to the Regulation of Minister of Transportation No. 36/2011, level crossing must be equipped with signs, markers and traffic signaling devices and crossing gate guards. However, 4600 of the 5800 level crossing points are without railroad keeper so that they are prone to traffic accidents. In addition, hazard information (danger signs) from the railroad keeper to the PUSDALOP and machinists sometimes cannot be seen at night and in a foggy situation. Therefore, this research aims to detect obstacles (cars) at a railroad crossing using the Histogram of Oriented gradient (HOG) method and the Support Vector Machine (SVM) classifier. HOG functions to extract object features (cars) while SVM is responsible for classifying car objects whether they fit the criteria of car features or not. The results show that an accuracy rate of car objects was 85% 73% for empty train tracks and 91% for detection of passing trains.
Ahmad Sugiana Bandiyah Sri Aprillia Maria Rifqi
189212661
Vehiculo autonomo a escala para el seguimiento de marcas viales de carretera
espanolEn este articulo se presenta el desarrollo de un vehiculo a escala que permite su conduccion autonoma mediante el auto guiado a traves de la deteccion de las marcas viales de la carretera. El vehiculo se basa en un modelo de radiocontrol modificado al que se le ha anadido una camara para la deteccion de marcas viales y semaforos, y un laser lidar para la deteccion de obstaculos. Ademas, incluye un procesador Odroid con sistema base Linux Ubuntu y ROS (Robot Operation System) para el control de alto nivel, y un microcontrolador Arduino para el control de los motores. El sistema de control esta basado en el modelo de arquitectura Subsumption en el que se incluyen tres modulos con diferentes niveles de prioridad. El seguimiento de marcas viales utiliza un algoritmo RANSAC para la deteccion de rectas y un sistema de control PID. El sistema de control incluye la deteccion de obstaculos y maniobras de adelantamiento, y la deteccion de semaforos. La arquitectura propuesta ha sido verificada en la edicion 2017 de la Seat Autonomous Driving Challenge alcanzando la primera posicion. EnglishThis article presents the development of a vehicle at scale that allows autonomous driving by self guided through the detection of road signs. The vehicle is based on a modified radio control model with a camera for the detection of road signs and traffic lights, and a lidar laser for obstacle detection. It also includes an Odroid processor with Ubuntu Linux base system and ROS (Robot Operation System) for high level control, and an Arduino microcontroller for motor control. The control system is based on the Subsumption architecture model, which includes three modules with different priority levels. The tracking of road markings uses a RANSAC algorithm for the detection of straight lines and a PID control system. The control system includes the detection of obstacles and maneuvers, and the detection of traffic lights. The proposed architecture has been verified in the 2017 edition of the Seat Autonomous Driving Challenge reaching the first position.
Actas de las XXXIX Jornadas de Automatica, Badajoz, 5 7 de Septiembre de 2018
Eduardo Zalama Casanova Eusebio de la Fuente Lopez Jaime Gomez Garcia-Bermejo