Abstract: This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Abstract: Vickers indentation is used to measure the hardness
of materials. In this study, numerical simulation of Vickers
indentation experiment was performed for Diamond like Carbon
(DLC) coated materials. DLC coatings were deposited on stainless
steel 304 substrates with Chromium buffer layer using RF Magnetron
and T-shape Filtered Cathodic Vacuum Arc Dual system The
objective of this research is to understand the elastic plastic
properties, stress strain distribution, ring and lateral crack growth and
propagation, penetration depth of indenter and delamination of
coating from substrate with effect of buffer layer thickness. The
effect of Poisson-s ratio of DLC coating was also analyzed. Indenter
penetration is more in coated materials with thin buffer layer as
compared to thicker one, under same conditions. Similarly, the
specimens with thinner buffer layer failed quickly due to high
residual stress as compared to the coated materials with reasonable
thickness of 200nm buffer layer. The simulation results suggested the
optimized thickness of 200 nm among the prepared specimens for
durable and long service.
Abstract: This paper presents an effective traffic lights
recognition method at the daytime. First, Potential Traffic Lights
Detector (PTLD) use whole color source of YCbCr channel image and
make each binary image of green and red traffic lights. After PTLD
step, Shape Filter (SF) use to remove noise such as traffic sign, street
tree, vehicle, and building. At this time, noise removal properties
consist of information of blobs of binary image; length, area, area of
boundary box, etc. Finally, after an intermediate association step witch
goal is to define relevant candidates region from the previously
detected traffic lights, Adaptive Multi-class Classifier (AMC) is
executed. The classification method uses Haar-like feature and
Adaboost algorithm. For simulation, we are implemented through Intel
Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and
rural roads. Through the test, we are compared with our method and
standard object-recognition learning processes and proved that it
reached up to 94 % of detection rate which is better than the results
achieved with cascade classifiers. Computation time of our proposed
method is 15 ms.