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Sensors and Materials, Vol. 35, No. 10 (2023) 4653–4669
MYU Tokyo
S & M 3420

An Improved Faster Region-based Convolutional Neural Network
Algorithm for Identification of Steel Coil End-head
Jian-Zhou Pan,1,2,3 Chi-Hsin Yang,2* Long Wu,2
Wen-Hu Tang,4 and Kung-Chieh Wang2
1School of Material Science and Engineering, University of Science and Technology,

Beijing, Beijing 100083, China

2School of Mechanical and Electrical Engineering, Sanming University,

Sanming, Fujian 365004, China

3Fujian Sansteel (Group) Co., Ltd., Sanming, Fujian 365000, China

4School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China(Received July 14, 2023; accepted October 5, 2023)

Keywords: steel coil end-head, improved faster region-based convolutional neural network (F-RCNN)
algorithm, deep learning, feature pyramid network (FPN), parallel attention module (PAM)

A method that uses machine vision and machine learning technologies to identify the endhead in a steel coil has seldom been proposed. In this study, an improved faster region-based
convolutional neural network (F-RCNN) deep learning algorithm is introduced to identify the
position of the steel coil end-head for a hardware system set up for image sensing and detection.
The feature pyramid network (FPN) and the parallel attention module (PAM), which are both
involved in the traditional F-RCNN, are used to increase the detection accuracy. Our
experimental results validated the effectiveness of the proposed improved algorithm.

1.

Introduction

Because of the importance of steels coils as both products and raw materials in construction
and industrial production, the demand for steel coils has recently been increasing. In the
production of a steel coil, including the manufacturing processes of heating, rolling, spinning,cooling, and other processes,(1) the internal structural composition of steel coils is not uniform,
causing the tensile strength, yield strength, and other mechanical properties of several turns ofcoil near the steel coil end-head to be unstable. Several turns of coil must then be cut andrecycled in accordance with the production regulations of the factory.
The drawbacks of the current production methodology for steel coils are summarized
below.(1)(1) The task of finding and cutting off the steel coil end-head is completed manually. The overall
efficiency of production is affected by increases in labor costs.
(2) The working environment of a steel-coil factory involves high temperature and high risk. It is
not suitable for the worker to continuously operate the task in (1) over a long duration.
*Corresponding author: e-mail: 20190207@fjsmu.edu.cn

https://doi.org/10.18494/SAM4589

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 35, No. 10 (2023)(3) Because of (1), the automation of the production process for steel coils is difficult to realize.
According to the above analysis, the most critical problem in the production of steel coils is theintelligent identification and positioning of the steel coil end-head.
In current industrial applications, the detection and recognition of objects are achieved byusing images of the objects, which are taken by industrial cameras, along with the appropriate
object recognition algorithms in machine vision. Object recognition algorithms are basicallydivided into traditional and deep-learning-based methods.(2) Traditional object recognition
algorithms mainly include template or feature matching. The main idea is that some featurepoints of the detected target are extracted by the designed algorithm, and then, similarity
measurement is used to match feature points between target images.(3) With the rapid
development of computer network algorithms in recent years, deep learning object detection
algorithms have made great progress. Compared with the traditional object recognition
algorithms, the detection accuracy and robustness of the deep learning object detection
algorithms are improved. They are widely applied in social distance monitoring in response to
the COVID-19 pandemic,(4) flight scene analysis,(5) autonomous driving,(6) environmental
monitoring,(7) and other fields.
In the literature, the deep learning object detection algorithms are divided into the singlestage and two-stage detection methods. The single-stage detection method includes the youonly-look-once (YOLO) series,(8–11) a single-shot multi-box detector (SSD),(12,13) and
MobileNet.(14) The algorithm directly performs convolutional network operations based on awhole image. Then, the category of the detected object and the position of the surrounding
border are predicted. However, the single-stage detection method easily enables false andmissing detection in small-target detection. The two-stage detection method mainly includes thefast region-based convolutional neural network (RCNN)(15) and improved faster RCNN
(F-RCNN).(16,17) The traditional F-RCNN introduces a regional suggestion network to classify
and locate the target twice to improve the detection accuracy. In addition, to improve thedetection accuracy of the algorithm in small-target detection, many improvements were
proposed. The combination of the SSD algorithm with the feature pyramid improved thedetection ability of small objects.(18,19) Huang et al. modified the network structure of the
traditional F-RCNN by adding both low-level and high-level features for target detection.(20)
In previous works, applications of the deep learning object detection algorithms in the field ofsteel materials were basically for detecting defects on the surfaces of steel products,(21,22)
irregular structure weld defects,(23) and weld missing defects.(24) However, the identification of
the steel coil end-head by applying the deep learning object detection algorithms is seldom
proposed in current studies of the steel industry. The position of the steel coil end-head within
the whole bundle of coils is unknown in advance and the distinction is not obvious. The
identification of the steel coil end-head is a problem of small-target detection.(20) Motivated by
the desire to mitigate the aforementioned drawbacks of the current steel-coil production process,in this study, we aim to present the improved F-RCNN-based algorithm, which is a machinelearning technology used in the image detection hardware system of steel coils, for theidentification of the steel coil end-head. Our algorithm is based on the traditional F-RCNN, andthe feature pyramid network (FPN) is added to the network model to detect fusion features, andSensors and Materials, Vol. 35, No. 10 (2023)

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the parallel attention module (PAM) is used to improve the quality of fusion features and thequality of preselected frames generated by the regional suggestion network, so as to improve thedetection accuracy of the algorithm.
The rest of the paper is organized as follows. In Sect. 2, the network structure of the
traditional F-RCNN is briefly introduced and the problems in the identification of the steel coil
end-head using the traditional F-RCNN are described. The main idea of the improved F-RCNNbased algorithm is given and the model training and analysis are described in Sect. 3. In Sect. 4,
the details of the identification of the steel coil end-head, including the setup of the steel coil
end-head detection hardware system, the augmentation of captured images, the model trainingof the proposed improved F-RCNN, and the detected results and discussion, are provided.
Finally, our conclusions are presented in Sect. 5.

2.

Network Structure of Traditional F-RCNN

2.1

Basic principle of F-RCNN

The traditional F-RCNN is divided into four parts, as shown in Fig. 1. The functions of eachpart are summarized below.(15,16,25)(1) Feature extraction network
The feature map is extracted from the input image by the feature extraction network, which isconstructed from the selected convolutional neural network. The extracted feature map isshared with the regional proposal network (RPN) and the region of interest (RoI) poolinglayer. Because only a small number of pixels are involved in small targets, the feature
extraction network is built by applying ResNet50(13) to generate a better feature map in thisstudy. After the input image is processed by ResNet50, the feature map with 512 channels is
obtained.
(2) RPN
The RPN generates anchor boxes of different sizes in accordance with the set values ofparameters in the network. Furthermore, it also determines whether each anchor box belongs

Fig. 1.

(Color online) Structure of traditional F-RCNN.

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to the foreground or background. Then, all anchor boxes are divided into positive andnegative samples. Finally, the appropriate candidate frame is obtained by correcting the offsetof the anchor box by regression.
(3) RoI pooling layer
The target candidate regions are extracted by feeding the feature map in (1) and the anchor
boxes from (2) into the RoI pooling layer.
(4) Classification and regression layers
The obtained target candidate regions are further classified by the classification andregression layers. The final detected images, where the position of the steel coil end-head is
accurately detected, are the outputs of the layers.
The model optimization of the F-RCNN is accomplished by iteratively updating the network
parameters to minimize the loss function. The loss function for optimizing the model of the
F-RCNN is defined as

L ({ pi } , {ti } ) =

(

)

(

)

1
1
Lcls pi , pi* + λ∑∑ pi*Lreg ti − ti* ,N cls iN reg i(1)where λ is the weighting balance value, i is the anchor index, pi is the probability that the anchor
is predicted as the object, pi* is the category label, ti is the predicted value of position regression,and ti* is the value of the label of position regression.
The category labels pi* = 1 for the positive sample and pi*= 0 for the negative sample, where
the sample is classified as a positive sample when the value of the intersection over union (IoU)
is greater than 0.7. Otherwise, the sample is classified as a negative sample when IoU is lower
than 0.3. Ncls and Nreg are the normalized loss values of classification and regression,respectively, where the index cls is the size of the batch and the index reg is the number ofanchor locations. Lcls, the loss function of cross entropy for classification, is defined as

(

)

(

)

Lcls pi , pi* =− log  pi* pi + 1 − pi* (1 − pi )  .



(2)

Lreg(x) is the positional regression function, which is defined as2
x <1 0.5 x ,Lreg ( x ) =  x − 0.5. otherwise

2.2

(3)

Problems in identification of steel coil end-head by F-RCNN

Because the position of the steel coil end-head cannot be known in advance, the images
captured by the cameras must cover the whole bundle of steel coil such that a captured image can
include all possible locations of the steel coil end-head. Generally, the steel coil end-head
occupies a very small pixel area in the captured image. Then, the identification problem of thesteel coil end-head is a problem of small target detection.(26)Sensors and Materials, Vol. 35, No. 10 (2023)

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When using the traditional F-RCNN to identify the position of the steel coil end-head in a
captured image, the following drawbacks are found:(1) The feature map from the last layer of the feature extraction network is only detected in the
traditional F-RCNN. With the increases in the number of feature extraction network layers
and down-sampling and pooling operation times, the feature map contains less detailed
information about the steel coil end-head in the last layer of the feature extraction network.
This is the cause of the poor detection and recognition results when directly applying the
traditional F-RCNN for the identification.
(2) When the pixels of the steel coil end-head are similar to the environmental pixels in thecaptured image, fewer distinguishing features are extracted by the traditional F-RCNN,
leading to a greatly reduced detection resolution. When the traditional F-RCNN is applied todetermine the location of the end-head from captured images with a large difference between
pixels of the steel coil end-head and the environment, the detection resolution is high, asshown in Figs. 2(a) and 2(b). However, for the captured image with very similar pixels
between the steel coil end-head and the environment, as shown in Fig. 2(c), the detection
resolution is poor.

3.

Improved F-RCNN-based Detection Algorithm

3.1

Improvement of traditional F-RCNN

To overcome the aforementioned drawbacks of underlying information loss during downsampling when applying the traditional F-RCNN in the identification of the steel coil end-head,
the improved F-RCNN-based detection algorithm is introduced in the study. The structure of the
improved F-RCNN is shown in Fig. 3. The main difference between the traditional F-RCNN andthe proposed improved F-RCNN is the inclusion of the FPN and PAM in the latter.

(a)

(b)

(c)

Fig. 2. (Color online) Results of steel coil end-head detection by traditional F-RCNN when pixels of the steel coil
end-head and environment are (a), (b) different and (c) similar.

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Fig. 3.

(Color online) Structure of the improved F-RCNN model.

The purpose of adding the FPN(27,28) into the structure of the improved F-RCNN is toincrease the detection and recognition accuracy of small targets by detecting multiple fusedfeature maps. The purpose of adding the PAM into the structure of the improved F-RCNN is tostrengthen the feature extraction of the target region. In Fig. 3, the PAM is added after the first
convolution layer of the RPN to obtain a high-quality preselection frame, which improves thedetection and positioning accuracy of the network. In the RoI Align network, the fusion featureand the preselected frame are pooled to obtain a 7 × 7 feature map. Finally, the classification andregression layers are used for identification and border positioning.
3.2

FPN-based feature extraction network

When the number of layers of the traditional F-RCNN is increased, the size of feature pixels
for the small target gradually decreases during feature extraction. This further reduces thedetection accuracy of the final feature map. The function of the FPN in the improved F-RCNN isto extract and fuse the features of the different layers in the feature extraction network so as toimprove the detection performance of small target objects. The schematic diagram of the FPN isshown in Fig. 4. It consists of a bottom-up model (left side), a top-down model (right side), andlateral connections.
The structure of the FPN-based feature extraction network based on ResNet50(13) is shown in
Fig. 5. The functions of the FPN are applied in the proposed improved F-RCNN to obtainbottom-up semantic features through horizontal connections of the features of five convolution
layers in the ResNet50 network. After the double up-sampling, the upper layer features are fusedwith the shallow layer features, and five layers of fusion features are obtained finally. The
feature map is converted by the last 1 × 1 convolution layer into the 256-dimensional feature
map. In addition, the 5-layer fusion features are fed into the RPN. Then, the detection accuracyof the proposed improved F-RCNN for small targets, such as the steel coil end-head, is improved
by applying the rich detail information in the feature maps of different scales.

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Fig. 4.

Fig. 5.

(Color online) Schematic diagram of FPN.

(Color online) Structure of FPN-based feature extraction network.

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PAM

To improve the quality of the generated preselection frame of feature extraction for the areaof the steel coil end-head, the PAM is added to the proposed improved F-RCNN. The traditional
F-RCNN already includes the cascade attention module (CAM), in which the spatial attention(SA) and channel attention (CA) modules are connected in cascade.(29–31) The principle of the SA
and CA modules is that, in accordance with the required focus, the human visual system will
focus on certain parts of a specific field of vision. The corresponding algorithms were developed.
In previous studies,(29–31) the CAM is applied to improve the effectiveness of the F-RCNN. In
this study, the PAM, where the SA and CA modules are parallelly placed, is added into the
proposed improved F-RCNN to increase the detection accuracy for the steel coil end-head.
The structure of the PAM in the proposed improved F-RCNN is shown in Fig. 6. The CA,SA, and input feature are arranged in parallel. In the PAM, the input feature F is fed to the CA
and SA after convolution processing. Then, the outputs of the CA and SA are spliced with theoriginal input feature by adjusting the number of feature channels to output the refined feature.
The function of the CA module is to obtain the internal relationships between the featurechannels. The operation of the CA module is depicted in Fig. 7. Firstly, global average pooling,
AvgPool(F), and maximum pooling, MaxPool(F),(32) are used to obtain the feature spatial
information. Then, the multilayer perceptron (MLP) is applied to process the two results ofglobal average pooling and maximum pooling. The output feature vector Mc is performed withpixel-by-pixel aggregation. Furthermore, each channel weight of the input feature is obtained

Fig. 6.

Fig. 7.

(Color online) PAM in the improved F-RCNN.

Schematic diagram of the operation of CA module.

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after processing by the sigmoid activation function. Equation (4) is the evaluated formula of theoperation of the CA module.

(=
M c σ MLP ( AvgPool ( F ) ) + MLP ( MaxPool ( F ) )

( (

)

(

= σ W2 W1 ( Avg ( F ) ) + W2 W1 ( Max ( F ) )

))

)

(4)

Here, σ represents the sigmoid function and F is the input feature. W1 and W2 are the weights
connected by the ReLU activation function.(33) Avg(F) and Max(F) are for the averaging
operation and taking the maximum of F, respectively.
The function of the SA module is to obtain the internal relationships between the feature
spaces. The operation of the SA module is depicted in Fig. 8. The SA module is applied to stack
the maximum value and the average value of the feature points. Then, the number of channels of
the feature map is adjusted by convolution processing, which is also used to strengthen andsuppress the features in different positions of the feature map. The weights of each feature pointof the input steel coil end-head feature map are also obtained by processing the sigmoid
activation function. Equation (5) is the evaluated formula of the operation of the SA module.
Ms

(

(

))

(

(

σ=
f 7×7  AvgPool ( F ) ; MaxPool ( F ) σ f 7×7  Avg ( F ) ; Max ( F ) 

))

(5)

σ represents the sigmoid function, F is the input feature, and f 7×7 stands for a convolution
operation with a convolution kernel of 7.(34,35)
In the PAM, channel attention, spatial attention, and the input feature are arranged in parallel.
Channel attention, which focuses on the features, acquires the internal information of the featurechannel. Spatial attention, which focuses on the positions of feature processing, obtains theinternal information of the feature space. To prevent the loss of the original input, the original
input is multiplied pixel by pixel with the processing of the channel and spatial attentions. At theFig. 8.

Schematic diagram of the operation of the SA module.

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same time, to avoid damage of the feature structure, the processing result is stacked with the
input and fed to the convolution layer to adjust the number of channels and then output. The
evaluated formula of the operation of the PAM is
M PAM = W3 [ M c ⊗ F ; M s ⊗ F ; F ] ,F = W0 ( x ) ,(6)

where ⊗ represents multiplying the elements one by one and F is the feature after the first
convolution. Mc and Ms are the operational formulas of the CA and SA modules defined by Eqs.
(4) and (5), respectively. x is the model input. W3 is the weight of the last convolution layer, and
W0 is the weight of the first convolution layer of the improved F-RCNN.
3.4

Model training and analysis of results

The experimental running environment is the Windows system with the GPU RTX3080, 10
Gb of memory, and Intel i9-10920x CPU. The GPU accelerates the use of CUDA10.0 and
CUDNNv7.6.4. The framework is PaddlePaddle and the programming language Python isapplied to develop the whole software program.
To verify the effectiveness of the improved F-RCNN with the added FPN and PAM in the
study, the Pascal VOC2012 dataset is applied to train the proposed model. In addition, thetraditional F-RCNN and the traditional F-RCNN with the FPN are also trained at the same time
to compare the improvement effect of model accuracy before and after model training. The
number of iterations for model training is set to 50 and the learning rate is set to 0.002. The
stochastic gradient descent (SGD) optimization algorithm is used in model training. The learning
rate decreases gradually, the batch size is 3, and the number of iterations for warming up is set to10.
Pascal VOC2012 is an open dataset containing a total of 20 categories, for example, people,
aircraft, and birds. In the study, in accordance with the numbers of training set:test
set:verification set data strokes, the Pascal VOC2012 dataset is divided into 8:1:1. The loss
functions of the improved F-RCNN model with the FPN and PAM, the F-RCNN model with theFPN, and the traditional F-RCNN model obtained by transfer learning using coco pretraining
parameters with 50 iterations of training are shown in Fig. 9.
It is shown that the loss functions of the three types of F-RCNN models for each learning
epoch decreased rapidly at the beginning of the training process and tended to be stable at theend. This means that model learning is effective and fit to a stable degree. Furthermore, for the
proposed improved RCNN with the FPN and PAM, the loss function converges to about 0.14
after 20 iterations of training.
The three types of F-RCNN models are set with the optimal weights obtained after modeltraining, and the optimal models are applied to the test dataset. The mean average
precision (mAP) values of the three types of F-RCNN models, obtained by applying the test
dataset, are listed in Table 1, where mAPIoU=0.5, mAPIoU=0.75, and mAPIoU=0.5:0.95 have theselected IoU thresholds of 0.5 and 0.75, and IoU= 0.5:0.05:0.95, respectively.

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Fig. 9.

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Loss functions of three types of F-RCNNs.

Table 1mAP values of three types of F-RCNN model.
mAPIoU=0.5
F-RCNN
60.97
F-RCNN (FPN)
62.23
F-RCNN (FPN + PAM)
63.11

mAPIoU=0.75
37.64
41.42
41.79

mAPIoU=0.5:0.95
35.69
38.24
38.73

Compared with the traditional F-RCNN, mAPIoU=0.5 improves from 60.97 to 63.11%,
mAPIoU=0.75 from 37.64 to 41.796%, and mAPIoU=0.5:0.95 from 35.69 to 38.73% for the proposed
improved F-RCNN. The addition of the FPN improves the accuracy of the traditional F-RCNNby about 3% on average and that of the PAM improves the accuracy of network detection by
about 1% on average. The proposed improved F-RCNN network with different thresholds
improves by about 4% on average.

4.

Identification of Steel Coil End-head

The improved F-RCNN described in the previous section is a kernel network algorithm used
to identify the steel coil end-head. For the deep learning of the improved F-RCNN, the open
image dataset is not yet available for the identification. To train the improved F-RCNN, theimage detection hardware system is built to collect the steel coil pictures in actual production in
the factory. After removing the images that are fuzzy and unintelligible, have no steel coil endhead, and do not cover the whole bundle of steel coils, 3653 image samples of steel coils are
obtained. The Labeling software is used to label the steel coil end-head to mark the coil head in
the image sample.
4.1

Setup of steel coil end-head detection hardware system

In the study, a schematic diagram of the steel coil end-head image detection hardware system
is shown in Fig. 10. The hardware of the detected system is mainly composed of a type C hook, adrum expansion mechanism, and a machine-vision system module. The newly produced steel

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Fig. 10. (Color online) Schematic diagram of the steel coil end-head detection system.

coil is suspended on the type C hook and moved from the coil collecting station to the front of
the drum expansion mechanism.
The drum expansion mechanism is composed of a drum expansion and a mobile platform
with a moving track. The machine-vision system module mainly consists of five industrial
cameras and the camera mounting bracket with a moving guide rail. The camera mounting
bracket with the moving guide rail is moved along the central axis of the drum expansion
mechanism to scan the steel coil, and images of the steel coil end-head are captured by the five
mounted industrial cameras. Then, the images are spliced and fed to the proposed improved
F-RCNN to evaluate and identify the location of the steel coil end-head.
A schematic diagram of the five industrial cameras around the drum expansion mechanism isshown in Fig. 11. The origin of the coordinate system (the x–y plane) is located at the center ofthe drum expansion mechanism and the z-axis coincides with the central axis of the drum
expansion mechanism. The coordinates (in mm) of the five cameras 1 to 5 are (887.08, −700),(935.64, 648.14), (0, 1130), (−925.64, 648.14), and (−887.08, −700), respectively, in the x–y plane.
The procedures for the identification of the steel coil end-head are summarized as follows.
(1) The steel coil suspended on the type C hook is transported from the coiling station and stops
in front of the drum expansion mechanism.
(2) To scan and detect the steel coil end-head, the drum expansion mechanism is moved forward
and backward by the mobile platform with a prespecified number of expanding and shrinking
movements.
(3) After the scanning and detecting tasks, the images taken by the five industrial cameras,
which are arranged around the circumference of the drum expansion mechanism, are spliced
as the input image and fed to the proposed improved F-RCNN for the identification of thesteel coil end-head.
(4) If the steel coil end-head is not identified, return to the previous step and repeat the scanningand detecting tasks. Otherwise, if the steel coil end-head is detected, three-dimensional
position coordinates are obtained.

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Fig. 11. Schematic diagram of the five industrial cameras.

4.2

Augmentation of captured images of steel coil end-head

Captured images only include one steel coil end-head, and there is not enough pixel data for
identification. Besides, there are only a few features that can be used to distinguish between thesteel coil end-head and the coil. Thus, a poor generalization ability of the overfitting networkmodel easily results during network training.
In the captured images, the pixels of the steel coil end-head and the edge of the steel coilclearly differ. This causes the gradient information of the edge of the steel coil to be greatly
different. The gradient information refers to the pixel difference in the image. The pixel
difference is calculated to scan the input image through the window function and further
evaluate the convolution. The gradient information of the edge of the steel coil, named the edge
gradient, is an important feature for distinguishing the steel coil end-head from the steel coil. In
the study, the edge gradient is extracted and added to the original captured image, and combinedwith the horizontal and vertical flips of the original steel coil end-head image to achieve theaugmentation of the captured images.

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Figure 12 shows selected augmented captured images. The augmented images can enhance
the robustness and detection performance of the trained improved F-RCNN. The steps for theargumentation of the captured image of the steel coil end-head shown in Fig. 12(a) are as follows.
(1) The gradient information in the x- and y-directions of the captured image is extracted to form
the gradient maps by the Sobel operator.(36,37)(2) OTSU threshold processing(38–40) is performed for the gradient maps in the x- andy-directions, and the pixels of the two maps are added item by item.
(3) The 3 × 3 kernel function is applied to perform the open operation processing on the gradientmap to eliminate image noise. A typical result is shown in Fig. 12(b).
(4) Pixels are added item by item to the gradient map and the original image to enhance the edge
features of the steel coil end-head in the original captured image.
(5) Flip the picture, as shown in Fig. 12(c) for a vertical flip and in Fig. 12(d) for a horizontal flip.

(a)

(b)

(c)

(d)

Fig. 12. (Color online) Augmentation of the captured images. (a) Original captured image. (b) Gradient image by
eliminating noise (c) Vertical flip image. (d) Horizontal flip image.

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Training-improved F-RCNN for steel coil end-head identification

After the aforementioned argumentation, the augmented image dataset of the steel coil endhead is divided into 8:1:1 for training set:verification set:test set. During the training of the
F-RCNNs, the picture size of each steel coil image is converted to 800 × 800. Three F-RCNNs
are trained in 50 iterations with coco pretraining parameters, and the values of loss function arerecorded. The curves of loss functions during network training are depicted in Fig. 13. It isshown that, with the progress of training, the loss function of the improved F-RCNN for the steel
coil end-head decreases continuously before 50 iterations of training. Furthermore, the improved
F-RCNN tends to be stable after more than 20 iterations of training. This indicates that thedetection model tends to converge.
The curves of detection accuracy in the verification set for the three F-RCNNs after modeltraining are depicted in Fig. 14. It is shown that the improved F-RCNN has higher mAPs than theother two F-RCNNs with different thresholds in the verification set. This means that the
proposed improved F-RCNN can better improve the image recognition accuracy in theFig. 13. Loss functions of F-RCNNs.

Fig. 14. (Color online) Curves of detection accuracy
for F-RCNNs.

Fig. 15. (Color online) Results of identification of steel coil end-heads by proposed improved F-RCNN.

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identification of the steel coil end-head. For the proposed improved F-RCNN, the results of theidentification of the steel coil end-heads in the sampled pictures are shown in Fig. 15. Comparedwith the detection results of the traditional F-RCNN(16,17) shown in Fig. 2(c), the proposed
F-RCNN exhibits a high resolution for the recognition of the steel coil end-heads.

5.

Conclusions

For the image detection hardware system of steel coils, an improved F-RCNN-basedalgorithm for the identification of the steel coil end-head was addressed in this study. In the
traditional F-RCNN, the FPN and PAM are added to the network model to improve the detection
accuracy of the algorithm for small target detection. Additionally, since the captured steel coilend image data is not enough for model training, three types of image are processed to achieve
the augmentation of captured images. That is, the image is mapped to the original captured
image by adding the edge gradient, and the images are flipped to the original image by
horizontal and vertical. The experimental results indicate that the proposed algorithm canfurther improve the image recognition accuracy in the identification of steel coil end-heads.

Acknowledgments
This work was carried out as part of the Major Science and Technology Projects of Fujian
Province (Grant no. 2022HZ026025), the Project of Department of Science and Technology of
Fujian Province (Grants nos. 2023-T-5001, 2021-H-0060, 2021-G-02013, and 2020-H-0049), theProgram for Innovative Research Team in Science and Technology in Fujian Province University,
the Production and Research Collaboration with Innovation in Key Scientific and Technological
Project of Sanming City (Grant no. 2022-G-17), and the Operational Funding of the Advanced
Talents for Scientific Research (Grants no. 19YG04 and 19YG05) of Sanming University. The
authors also acknowledge the support from the University of Science and Technology Beijing
and the School of Mechanical and Electric Engineering, Sanming University.

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