Instrumentation & Measurement Magazine 24-2 - 96

Fig. 2. Reliable features such as roundness depicted in (a) and (b); Possible
illumination problems that can be caused by light reflections shown in (c) and (d).

resources not accessible to all research groups. For this reason,
a popular training strategy is to use transfer learning techniques for adapting networks pre-trained on public databases
to custom image data-sets [38].

Hardware and Preprocessing Considerations
If we are starting with no data, this can be seen as an advantage as data collection can be done in such a way to facilitate
the implementation of a system. Take, for example, improper
illumination from light reflections. Fig. 2 shows an example
of this scenario. In this case, the objective is to grade tomatoes
into different quality categories. If the shape of a tomato is to
be important, that is, how round the object is, this feature can
be easily calculated using circle detection to find how many
pixels in the border of the tomato are within that circle. Fig.
2a and Fig. 2b show those normalized numbers to be 0.3434
and 0.1365, and this feature can be good enough and may not
require any level of sophistication in terms of what type of classifier to use. However, if defects on the tomato are also being
considered, then the reflection from the light source can present difficulties, as shown in Fig. 2c and Fig. 2d. This may be
solved by using a circular polarizer in the camera acquisition
system or diffusing the light source. A DL approach would
require thousands of images with different illumination possibilities so that the features can be found automatically under
less than optimum illumination.

Thus, no data can actually be a good start as better data
can be obtained, considering the patterns a network is about
to learn from the data set. Even preprocessing the data before
using a neural network can help. Take for example grading
dates. Fig. 3 shows color transformations of the gray scale
value images of dates by easily converting them into color using a colormap and linearly assigning a particular gray level
value to a specific value in the colormap table. The three targeted date categories are soft, semi soft and hard dates. Using
900 images, Fig. 3 shows how training accuracy is improved
when using a shallow CNN.
For real time applications, not only the complexity of the
classifier is in question, but also the hardware that will make
those decisions. If a stand-alone instrument is to be designed
as a second step of a successful proof of concept, a neural network can be very fast for decision making. As CNNs require
the calculation of features from the input images via convolutions, the calculations take more time. Here, there is a trade off
again, as a shallow conventional neural network can compute
an output extremely rapidly, but features need to be calculated
first and that can take some time, too. These features can be
calculated really quickly, though, using FPGAs. For example,
circle detection used in Fig. 2 can be implemented with a video
capturing board that includes a specialized FPGA [39]. Furthermore, in computer vision, features can be extracted from
images using morphological operations, which based on set
theory and working with binary images, can be computationally fast [40]. Thus, not only the complexity of the CNN is in
question, but also the hardware that will make those decisions.
Low-cost digital signal processing boards, such as the Texas
Instruments C5535/C5545 eZdsp USB Stick Development Kit,
can be used to analyze the auditory data in real-time and implement the matrix multiplication needed using a NN. For DL
solutions, Google Coral / Edge TPU boards can be used. The
costs are quite similar and all the computations can be carried
out on such devices.

Fig. 3. (a) Training accuracies. (b) Date images and colormaps used.
96	

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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