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28 April 2023

Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

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1
College of Chemistry and Materials Engineering, Hainan Vocational University of Science and Technology, Haikou 571156, China
2
Liaoning Key Laboratory of Chemical Additive Synthesis and Separation, Yingkou Institute of Technology, Yingkou 115014, China
3
SolBridge International School of Business, Woosong University, Daejeon 34613, Republic of Korea
4
Fulin Warehousing Logistics (Yingkou) Co., Ltd., Yingkou 115007, China
Processes2023, 11(5), 1357;https://doi.org/10.3390/pr11051357
This article belongs to the Special Issue Trends of Machine Learning in Multidisciplinary Engineering Processes
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Abstract

Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

1. Introduction

Steel surface defect detection is an important topic in material science research [1]. As one of the most important fundamental materials, steel contributes to numerous industry productions, such as airplanes, automobiles and high-speed railways. Among various steel productions, flat steel is the dominant product and contributes the most to industrial applications. As such, the quality of flat steel is vital for daily life.
Unfortunately, there are usually defects on flat steel surfaces, making it challenging to generate high-quality steel industrial productions. There are six typical defects on steel surfaces: crazing, inclusion, patches, pitted surface, rolled-in scale and scratches [2]. Figure 1 demonstrates the typical types of different steel surface defects in the North East University Detection (NEU-DET) dataset. The defects lead to bad quality in the flat steel, making it challenging to produce high-quality industrial productions.
Figure 1. Typical types of steel surface defects in the NEU-DET dataset: (a) Crazing. (b) Inclusion. (c) Patches. (d) Pitted Surface. (e) Rolled-in Scale. (f) Scratches.
In traditional factories, steel surface defect detection relies on human supervision, which is time-consuming and uneconomical [3]. On the one hand, extra supervisors require more resources than automatic detection does. On the other hand, human-dependent detection cannot ensure quality supervision around-the-clock. With the development of the industrial vision area, computers come to be a powerful tool to detect surface defects. Previous works rely on hand-crafted feature extractors and machine learning-based classifiers to localize the defects. An artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN) and other machine learning technologies have been widely applied in different steel surface defect detection methods [4,5,6]. However, these works suffer from lower precision and cannot satisfy the real-time speed requirement.
To boost the accuracy and improve the speed, there are convolutional neural networks (CNNs) especially developed for defect detection. Mustafa et al. used different image classification methods to recognize the diverse steel surface defects [7]. He et al. utilized a multi-level feature fusion network and classified the different kinds of defects [8]. These works demonstrate good performances with two-stage object detection networks, separating the localization and classification steps, which is time-consuming.
In contrast to two-stage networks, you-only-look-once (YOLO) series methods utilize one-stage objection detection technology and achieve real-time speed [9,10,11,12,13]. In particular, the fifth version, YOLO-v5, achieves the state-of-the-art detection performance which has been widely utilized in various situations, such as letter recognition [13], circuit defect detection [14] and fabric detection [15]. However, the traditional YOLO-v5 method cannot effectively explore steel surface defects. On the one hand, as shown in Figure 1, there are different representations of the defects, making it difficult to accurately localize and classify the defect areas. On the other hand, the defects vary from different scales. The extra small or large defects are challenging to explore and detect.
This paper proposes an improved multi-scale YOLO-v5 technology for real-time steel surface defect detection. In particular, we develop a multi-scale block to effectively explore the defects. Convolutions with different filter sizes are especially developed to process the input images and generate the multi-scale information. The multi-scale image features are aggregated by one convolutional layer for information fusion, which boosts the representation capacity of the network. Furthermore, a spatial-attention mechanism is developed to concentrate more on the defect areas and improve the detection accuracy. The experimental results show that the improved multi-scale YOLO-v5 method can more accurately detect the steel surface defects than the original version, which satisfies the real-time speed requirement.
Our contributions can be concluded as follows:
  • We propose an improved multi-scale YOLO-v5 network for effective steel surface defect detection, which achieves a high detection accuracy and demonstrates a good robust performance.
  • We develop the multi-scale block and spatial attention mechanism to process the steel surface images, which effectively explore the defect information and improve the accuracy of the network.
  • Experimental results show that the improved network has a higher prediction accuracy than the vanilla YOLO-v5 method, which satisfies the real-time speed requirement.

3. Method

In this section, we firstly introduce the design of the proposed network. Then, the multi-scale block and the spatial attention mechanism are described. Finally, we demonstrate the detailed implementation of the proposed network.

3.1. Network Design

Figure 2 shows the network design of the proposed improved multi-scale YOLO-v5 method. This network is composed of three different components: the bottleneck, the head and the detector. The input image is firstly processed by the bottleneck to explore the multi-scale features. Then, the proposed features are aggregated and further processed by the head. Finally, the multi-scale features are sent to the detector for classification and localization.
Figure 2. Network design of the proposed improved multi-scale YOLO-v5 method.
As shown in the figure, the bottleneck is composed of the combination of convolution, batch normalization and SiLU activation (CBS), the multi-scale sequence (MS) and the spatial pyramid pooling fusion (SPPF). There are five CBSs, fifteen MSs and one SPPF in the bottleneck. In MS, there are three multi-scale blocks (MBs) and one CBS for multi-scale feature fusion. The SPPF is composed of two CBSs and three max pooling (MaxPool) operations. The MaxPool operations explore the image feature in the spatial pyramid pooling fashion. Then, CBS combines and fuses the multi-scale features.
The head of the network is composed of four CBSs, twelve MSs and several bicubic operations to maintain the image resolution. The head combines features from the different stages of the bottleneck, and uses CBS and MSs for better multi-scale feature fusion. Finally, the multi-scale features of the head are sent to the detector for object detection and localization.
The detector follows the vanilla YOLO-v5 design [13], which regresses the bias of different anchors and localizes the objects. The detector contains three scales to effectively explore the small and large objects. For each scale, there are three anchors to localize the defects.

3.2. Design of the Multi-Scale Block and Spatial Attention

Figure 3a shows the design of the MB. There are two multi-scale convolutions (MSConv) to explore the hierarchical image information. After that, one CSB with skip connection builds the residual structure for better gradient transmission. Figure 3b demonstrates the design of MSConv. In the MSConv, two 1 × 1 and two 3 × 3 convolutions crossly process the image feature and explore the multi-scale information. After that, one 1 × 1 convolution combines the features of two convolutions for information fusion and keeps the number of channels. A spatial attention (SA) mechanism is specifically developed to further concentrate on the defect information and improve the detection performance. Finally, a skip connection is introduced for better gradient transmission.
Figure 3. Design of the multi-scale block (MB): (a) Multi-scale block (MB); and (b) multi-scale convolution (MSConv).
Figure 4 shows the design of the SA. In the figure, we can find that the SA has two convolutions, one ReLU activation and one sigmoid activation. The convolutions decrease and increase the channel number symmetrically. The ReLU activation introduces the non-linearity to the attention exploration. Finally, the sigmoid activation introduces the non-negativity to the attention.
Figure 4. Design of the spatial attention (SA) mechanism.
The spatial attention mechanism follows an encoder–decoder design, which can effectively explore the spatial correlation of the input image feature. The sigmoid activation brings the non-negativity to the feature and gives higher weights to the detected areas, which helps boost the network representation capacity and improve the detection accuracy.

3.3. Implementation Details

Table 1 shows the parameter settings of the proposed improved multi-scale YOLO-v5 network. The component index follows the order in Figure 2. The scale of CBS means decreasing the resolution of the image feature by s times, and the scale of bicubic means increasing the resolution by s times, where s is the scale.
Table 1. Parameter settings of the improved multi-scale YOLO-v5 network.
During the training phase, we used the same loss functions as the YOLO-v5, including the coordinate loss, the target confidence loss and the target classification loss. The weights and the implementation are entirely the same as the vanilla YOLO-v5 design for a fair comparison.

4. Experiment

4.1. Settings

We chose the NEU-DET [2] dataset to train and test our model. NEU-DET contains 1800 steel surface defect images with six typical defects: pitted surface, rolled-in scale, scratches, crazing, inclusion and patches. Among the images, we randomly chose 60% for training, 20% for validation and 20% for testing. We trained the network on one NVIDIA RTX 3080-Ti GPU. The batch size was chosen as 16. We updated the network for 100 epochs. The optimizer was chosen as Adam with a learning rate as 10 − 3 .
The measurements of the performance are chosen as precision, recall and mean average precision (mAP). The precision and the recall are defined as
P = T P T P + F P ,
and
R = T P T P + F N ,
where T P , F P and F N are the true positive, false positive and false negative samples, respectively. P is the precision and R is the recall.

4.2. Results

To demonstrate the effectiveness of our method, we mainly compared the improved version with two vanilla YOLO-v5 network settings: YOLOv5-s and YOLOv5-m. We firstly compared the computational complexity of different methods. Table 2 shows the parameters, GFLOPs and time costs of different methods. The GFLOPs are calculated by processing one 640 × 640 image. In the table, we can find that our method satisfies the real-time speed requirement and has the ability to process more than 190 images per second.
Table 2. Computational complexity comparisons among different methods.
To demonstrate the effectiveness of our method, we compared it with YOLO-v7tiny [68], one of the state-of-the-art object detection methods. Table 3 shows the precision, recall, mAP50 and mAP50-95 comparisons among the different object detection methods. In the table, we can find that our network achieves the highest scores on all testing indicators. From this point of view, our method can effectively detect the defects of the steel surfaces. Figure 5 shows the PR-curve among different methods. In the figure, we can find that our method has a larger area under the curve (AUC), which denotes a better performance than the other methods. To further investigate the effectiveness of our method, we also demonstrated the precision, recall and F1 curves. Figure 6 shows the results of different indicators. We can find that our method has a good performance on different kinds of defects. Finally, we demonstrated the visualized results of the steel surface detect detection. Figure 7 shows the comparison between ground-truth and our prediction results. In the figure, we can find that our method can predict most of the defects on the steel and has a robust performance in terms of defects with different scales.
Table 3. Precision, recall, mAP50 and mAP50-95 comparisons among different methods.
Figure 5. PR curve comparisons among different methods.
Figure 6. Precision, recall and F1 curves of our method.
Figure 7. Visualized results of the steel surface defect detection: (a–c) Groundtruth. (d–f) Prediction results. Zoom-up for better view.
The performance gain comes from the well-designed network architecture. In Table 2, our method has similar parameters, GFLOPs and time costs to YOLO-v5m. In contrast, the performance of our method is superior to YOLO-v5m. It should be noticed that YOLO-v5m is a larger version of YOLO-v5s, whose performance improvement is limited. From this point of view, the performance gain comes from the new architecture rather than the larger network.
It should be noticed that the best mAP50 performance of Table 3 is of approximately 0.72, which is lower than other reports. This is because we used an entirely different data organization protocol from other papers. In our work, the NEU-DET dataset is split by 60%, 20% and 20% for training, validation and testing, respectively. The amount of training data is much smaller than in other works for ensuring the generation performance. To fairly compare the effectiveness of different methods, we re-trained different methods under the same protocol, the results of which are reliable for measuring the performances.

5. Conclusions

In this paper, we proposed an improved multi-scale YOLO-v5 network for steel surface defect detection. To focus on diverse defects at different scales, we developed a multi-scale block to effectively explore the defects with different resolutions. To further improve the network performance and concentrate more on the defect areas, we developed a spatial attention mechanism to give higher weights to abnormal information. The experimental results show that the improved multi-scale YOLO-v5 network can effectively detect different kinds and scales of defects and satisfies the real-time speed requirement.

Author Contributions

Conceptualization, X.L. and L.W.; methodology, L.W.; software, X.L.; validation, J.M., W.S. and H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported in part by the Cooperation Innovation Plan of Yingkou for Enterprise and Doctor (QB-2019-10, 2022-13), the Liaoning Science and Technology Joint Fund (2020-YKLH-26, 2021-YKLH-19), the Foundation of Liaoning Key Laboratory of Chemical Additive Synthesis and Separation (ZJNK2109), the Program for Excellent Talents of Science and Technology in Yingkou Institute of Technology (RC201902) and the Liaoning Province’s Science and Technology Plan (Major) Project of “Jiebangguashuai” (2022JH1/10400009).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The dataset is public in the reference [2].

Conflicts of Interest

The authors declare no conflict of interest.

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