Convolutional neural networks for obstacle detection on the road and driving assistance

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Ramzi Mosbah ◽  
Larbi Guezouli
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Obstacle Detection ◽  
The Road ◽  
Driving Assistance ◽  
On The Road

scholarly journals Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5501 ◽  
Author(s):  
Chanjun Chun ◽  
Seung-Ki Ryu
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Supervised Learning ◽  
Convolutional Neural Networks ◽  
Surface Damage ◽  
Road Surface ◽  
Training Dataset ◽  
The Road ◽  
Fully Convolutional Neural Networks ◽  
On The Road

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

scholarly journals Road Pothole Detection using Deep Learning Classifiers

2020 ◽  
Vol 8 (6) ◽  
pp. 563-570 ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Data Collection ◽  
Convolutional Neural Networks ◽  
Road Maintenance ◽  
The Road ◽  
Wear And Tear ◽  
On The Road ◽  
Maintenance Process ◽  
Pothole Detection

Pothole is one of the major types of defects frequently found on the road whose assessment is necessary to process. It is one of the important reason of accidents on the road along with the wear and tear of vehicles. Road defects assessment is to be done through defects data collection and processing of this collected data. Currently, using various types of imaging systems data collection is near about becomes automated but an assessment of defects from collected data is still manual. Manual classification and evaluation of potholes are expensive, labour-intensive, time-consuming and thus slows down the overall road maintenance process. This paper describe a method for classification and detection of the potholes on road images using convolutional neural networks which are deep learning algorithms. In the proposed system we used convolutional neural networks based approach with pre-trained models to classify given input images into a pothole and non-pothole categories. The method was implemented in python using OpenCV library under windows and colab environment, trained on 722 and tested on 116 raw images. The results are evaluated and compared for convolutional neural networks and various seven pre-trained models through accuracy, precision and recall metrics. The results show that pre-trained models InseptionResNetV2 and DenseNet201 can detect potholes on road images with reasonably good accuracy of 89.66%.

CASA: An Alternative Smartphone-Based ADAS

2021 ◽  
pp. 1-41
Author(s):  
Manolo Dulva Hina ◽  
Hongyu Guan ◽  
Assia Soukane ◽  
Amar Ramdane-Cherif
Keyword(s):  
Low Cost ◽  
Electronic System ◽  
Time Data ◽  
Case Scenario ◽  
Safe Driving ◽  
The Road ◽  
Driving Assistance ◽  
Alternative Means ◽  
On The Road ◽  
Driving Assistance System

Advanced driving assistance system (ADAS) is an electronic system that helps the driver navigate roads safely. A typical ADAS, however, is suited to specific brands of vehicle and, due to proprietary restrictions, has non-extendable features. Project CASA is an alternative, low-cost generic ADAS. It is an app deployable on smartphone or tablet. The real-time data needed by the app to make sense of its environment are stored in the vehicle or on the cloud, and are accessible as web services. They are used to determine the current driving context, and, if needed, decide actions to prevent an accident or keep road navigation safe. Project CASA is an undertaking of a consortium of industrial and academic partners. A use case scenario is tested in the laboratory (virtual) and on the road (actual) to validate the appropriateness of CASA. It is a contribution to safe driving. CASA’s contribution also lies in its approach in the semantic modeling of the context of the environment, the vehicle and the driver, and on the modeling of rules for fusion of data and fission process yielding an action to be implemented. In addition, CASA proposes a secured means of transmitting data using light, via light fidelity (LiFi), itself an alternative means of wireless vehicle–smartphone communication.

Predictive Eco-Driving Assistance on the Road

2021 ◽  
pp. 205-226
Author(s):  
Craig K. Allison ◽  
James M. Fleming ◽  
Xingda Yan ◽  
Roberto Lot ◽  
Neville A. Stanton
Keyword(s):  
The Road ◽  
Driving Assistance ◽  
On The Road

scholarly journals A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

2020 ◽  
Vol 12 (5) ◽  
pp. 765 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramon Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Road Network ◽  
Ideal Solution ◽  
The Road

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

2020 ◽  
pp. 095440702094139
Author(s):  
Jun Liu ◽  
Rui Zhang ◽  
Shihao Hou
Keyword(s):  
Distance Estimation ◽  
Estimation Methods ◽  
Vanishing Point ◽  
Estimation Model ◽  
The Road ◽  
Attitude Angle ◽  
Driving Assistance ◽  
Lane Line ◽  
Texture Orientation ◽  
On The Road

Perceiving the distance between vehicles is a crucial issue for advanced driving assistance systems. However, most vision-based distance estimation methods do not consider the influence of the change in camera attitude angles during driving or only use the vanishing point detected by lane lines to correct the pitch angle. This paper proposed an improved pinhole distance estimation model based on the road vanishing point without the lane line information. First, the road vanishing point is detected based on the dominant texture orientation, and the yaw and pitch angles of the camera are estimated. Then, a distance estimation model considering attitude angle compensation is established. Finally, the experimental results show that the proposed method can effectively correct the influence of the camera attitude angle on the distance estimation results.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

Comparative Analysis Between Recurrent Convolutional and Convolutional Neural Networks for Horizon Detection

2020 ◽  
Vol 17 (9) ◽  
pp. 4364-4367
Author(s):  
Shreya Srinarasi ◽  
Seema Jahagirdar ◽  
Charan Renganathan ◽  
H. Mallika
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Semantic Segmentation ◽  
Unmanned Vehicles ◽  
Obstacle Detection ◽  
Ground Vehicles ◽  
Safe Navigation ◽  
Horizon Line ◽  
Flight Parameters

The preliminary step in the navigation of Unmanned Vehicles is to detect and identify the horizon line. One method to locate the horizon and obstacles in an image is through a supervised learning, semantic segmentation algorithm using Neural Networks. Unmanned Aerial Vehicles (UAVs) are rapidly gaining prominence in military, commercial and civilian applications. For the safe navigation of UAVs, there poses a requirement for an accurate and efficient obstacle detection and avoidance. The position of the horizon and obstacles can also be used for adjusting flight parameters and estimating altitude. It can also be used for the navigation of Unmanned Ground Vehicles (UGV), by neglecting the part of the image above the horizon to reduce the processing time. Locating the horizon and identifying the various obstacles in an image can help in minimizing collisions and high costs due to failure of UAVs and UGVs. To achieve a robust and accurate system to aid navigation of autonomous vehicles, the efficiency and accuracy of Convolutional Neural Networks (CNN) and Recurrent-CNNs (RCNN) are analysed. It is observed via experimentation that the RCNN model classifies test images with higher accuracy.

scholarly journals Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5560
Author(s):  
Chanjun Chun ◽  
Taehee Lee ◽  
Sungil Kwon ◽  
Seung-Ki Ryu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Road Safety ◽  
Estimation Method ◽  
The Road ◽  
Classification And Regression ◽  
Road Marking ◽  
Reference Plate

Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

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