Instrumentation & Measurement Magazine 24-2 - 94

job as they reported classification accuracies of 95%. Now, if
one would like to think of using newer techniques to possibly improve the 95% success classification, Deep Learning
(DL) comes to mind. This last discussion leads to a very good
question: is using DL a good idea for such applications? The
purpose of this paper is to guide readers towards making
such a decision as well as the different aspects that need to be
considered. For example, going back to the work presented
in [4], as 300 images were used, maybe this data set is not big
enough for implementation using a Convolutional Neural
Network (CNN) as it usually requires thousands of images to
train successfully. Having said that, we would like to present
this paper with a series of questions the reader may ask, on
the understanding that not all the questions may be relevant
to everybody and that some may be skipped.

Recent Use of DL in Machinery
What is Needed to Develop Proof of Concept?

Table 1 - Examples of pretrained CNN networks
Network

Depth (number of layers)

squeezenet

18

googlenet

22

inceptionv3

48

densenet201

201

mobilenetv2

53

resnet18

18

resnet50

50

resnet101

101

xception

71

inceptionresnetv2

164

shufflenet

50

darknet19

19

darknet53

53

8
DL has reached relevance in agricultural applications, for
example for avoiding obstacles in autonomous agriculture
vgg16
16
machines using CNN architectures such as Alexnet [5] and
vgg19
19
ICNet [6]. Another CNN, AMTNET, was used for automatic
nasnetlarge
1244
recognition of agricultural machinery images [7]. Regarding
the ability to implement a proof of concept system, there are
available pre-trained CNNs that can be used for these agri- training algorithms such as gradient descent, these values
cultural applications. Using free software such as PyTorch [8] adjust to a different application.
and TensorFlow [9], these networks can be modified to adAn important aspect to consider when selecting a prejust the number of classes needed for a specific application trained network is the time the network requires to obtain an
by modifying the last output layer. Additionally, the input output. From a study of driver responses that took place on
layer can be modified to fit the size of the data. Table 1 shows highways in personal vehicles covering a variety of driving
some of the different pre-trained CNNs available in Matlab conditions [12], [13], and assuming that reaction times to stimand Fig. 1 shows a comparison of these pretrained network ulus while driving farm machinery would be similar, we can
prediction times versus
their complexity using a
computer with a GPU card
[10]. A trade off is expected
between number of layers
and prediction time and
accuracy. As shown in Fig.
1, Alexnet offers the least
accuracy in the slowest
prediction time, and at the
other end of the spectrum
we found nasnetlarge. The
database used in Fig. 1 [11]
consists of general images
such as musical instruments, furniture, animals,
etc. but this is not an impediment for training a
pretrained network with
data for a different application, as during training,
the network starts with
Fig. 1. Comparison of different pretrained CNN networks in terms of accuracy, complexity (blue area size) and decisionmaking times.
random values and with
94	

alexnet

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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