5 research outputs found
Brake Light Detection Algorithm for Predictive Braking
There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m
Brake Light Detection Algorithm for Predictive Braking
There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m
Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier
This paper aims to enhance the ability to predict nighttime driving behavior
by identifying taillights of both human-driven and autonomous vehicles. The
proposed model incorporates a customized detector designed to accurately detect
front-vehicle taillights on the road. At the beginning of the detector, a
learnable pre-processing block is implemented, which extracts deep features
from input images and calculates the data rarity for each feature. In the next
step, drawing inspiration from soft attention, a weighted binary mask is
designed that guides the model to focus more on predetermined regions. This
research utilizes Convolutional Neural Networks (CNNs) to extract
distinguishing characteristics from these areas, then reduces dimensions using
Principal Component Analysis (PCA). Finally, the Support Vector Machine (SVM)
is used to predict the behavior of the vehicles. To train and evaluate the
model, a large-scale dataset is collected from two types of dash-cams and
Insta360 cameras from the rear view of Ford Motor Company vehicles. This
dataset includes over 12k frames captured during both daytime and nighttime
hours. To address the limited nighttime data, a unique pixel-wise image
processing technique is implemented to convert daytime images into realistic
night images. The findings from the experiments demonstrate that the proposed
methodology can accurately categorize vehicle behavior with 92.14% accuracy,
97.38% specificity, 92.09% sensitivity, 92.10% F1-measure, and 0.895 Cohen's
Kappa Statistic. Further details are available at
https://github.com/DeepCar/Taillight_Recognition.Comment: 12 pages, 10 figure
Primena inteligentnih sistema mašinske vizije autonomnog upravljanja železničkim vozilima
The railway is an important type of transport and has a significant
economic impact on the industry and people's everyday life. Due
to its capacities and complex infrastructure, it is necessary to work
on its constant development and improvement. Railway
automation requires the use of intelligent systems as a necessary
part of an autonomous railway vehicle. As from the point of view
of safe traffic, the existence of the object on the rail track and / or
in its vicinity represents a potential obstacle to the railway traffic,
and visibility has a very important role in correct and timely
detection of the object on the railway infrastructure, a key element
of autonomous railway vehicle is an obstacle detection system on
the part of the railway infrastructure, in conditions of reduced
visibility.
The subject of scientific research of this doctoral dissertation is the
application of intelligent machine vision systems in autonomous
train operation. For the purpose of detecting obstacles on the part
of the railway infrastructure in conditions of reduced visibility, a
thermal imaging camera and a night vision system are integrated
into the system, coupled with a developed advanced algorithm for
image processing with artificial intelligence tools. In addition, the
distance from the machine vision system to the detected object
was estimated. The operation of the system was tested in a series
of field experiments, at different locations, in different visibility
conditions and weather conditions, through realistic scenarios