500 550 600
Aluminium Copper Zinc Stainlesssteel Brass
650 700 750
800 850 900 950 1000
To perform the task of sorting
non-ferrous metals first requires
the material to be located and
then classified based on its spectral signature. These spectral signatures can then be used to identify the material. Aluminum,
copper, zinc, stainless steel and
brass metal all exhibit different
spectral response characteristics
in the visible and near infrared
(VNIR) spectrum (Figure 2). As
can be seen, brass and stainless
steel are the most difficult classes
to distinguish since their spectral
reflectance characteristics are similar. Because
of this, hyperspectral imaging systems must be
used to classify each individual material.
Before classification, however, non-ferrous
metals are tracked as they move through the
sorting system. In the system developed at the
University of Liège, this is performed using a
3D Ranger camera from SICK (Waldkirch,
Germany; www.sick.com) mounted above a
conveyor belt. Using this camera, images of
particles are segmented and the size and position of each metallic part is measured. Data
from the camera is then transmitted over a
Gigabit Ethernet interface to a host PC.
In the design of the system, a programmable
logic contorller (PLC) from Beckhoff (Verl,
Germany; www.beckhoff.com) tracks the conveyor progress and is used to trigger air blowers at the end of the conveyor. The incoming
signal from the encoder is interfaced to several
frequency dividers from Motrona (
Gottmadingen, Germany; www.motrona.com) and then
to the cameras, frame grabbers and the sorting devices’ PLCs. Software then sends positional coordinates to the robots’ PLCs through
a TCP interface such that there is no PLC
between the encoder and sorting devices/sen-sors, only frequency dividers.
“To classify non-ferrous metals, a signif-
icant portion of the reflectance spectrum
must be measured to maximize the system’s
discriminative potential,” says Barnabé. For
this reason, two hyperspectral cameras are
used to acquire images. These consist of a
MV1-D1312IE-Camera Link camera from
Photonfocus (Lachen, Switzerland; www.
photonfocus.com) with a spectral response in
the visible and near infrared (VNIR) region.
This camera is mounted with a Xenoplan
1.9/35mm broadband lens from Schneider
Optics (Hauppauge, NY, USA; www.schnei-
deroptics.com) coated for imaging between
400-1000nm. This camera is interfaced to a
host PC using a Camera Link frame grabber
from Imperx (Boca Raton, FL, USA; www.
imperx.com). The second camera, an SWIR
camera from Specim (Oulu, Finland; www.
specim.fi), is used to image the non-ferrous
metals in the 1000-2500nm spectrum and
is interfaced to the host PC using a Camera
Link frame grabber from National Instru-
ments (Austin, TX, USA; www.ni.com).
While both the cameras feature area array
sensors, they are instead used in line-scan (or
push-broom) mode. Coupled with a ImSpec-
torV10E spectrograph from Specim, the
VNIR camera images the metal-
lic particles such that the detec-
tor registers the spatial position
from a line and the spectral infor-
mation in each spatial position.
To maximize the amount of light
for each spatial pixel, spectral
binning is performed with the
Photonfocus camera, stacking
eight adjacent pixels. This has
the benefit of reducing the spec-
tral resolution of the sensor to the
same magnitude as the intrinsic
resolution of the spectrograph
(that is approximately 5nm).
To illuminate the metallic parts as they move along the conveyor
at speed, a high-level of illumination was
required. For this reason, an extruded elliptical reflector was used. This consists of an
array of Halogen lamps such that light from
the striking reflector are redirected to converge and produce a concentrated beam in a
linear fashion (Figure 3).
After line-scan images are captured by the
VNIR and SWIR cameras, they must be
merged. The spatial resolution for the VNIR
camera is 1312 pixels and 320 pixels for the
MCT-based SWIR cameras across the line-scan image. To merge these images, the value
of surrounding pixel’s geometric mean from
both the VNIR and SWIR camera is combined to produce a final image.
After images are captured by the system,
classification algorithms are used to analyze
Figure 2: Aluminum, copper, zinc, stainless steel and brass metal all
exhibit different spectral response characteristics in the very near infrared (VNIR) spectrum. Brass and stainless steel are the most difficult classes to distinguish since their spectral reflectance characteristics are similar.
Organizations and companies mentioned
Boca Raton, FL, USA
Austin, TX, USA
PR Sys Design
Delft, The Netherlands
Hauppauge, NY, USA
University of Liège