A deep learning model for steel surface defect detection

Li, Zhaoguo; Wei, Xiumei; Hassaballah, M.; Li, Yihong; Jiang, Xuesong

doi:10.1007/s40747-023-01180-7

Cited by 16 publications

(5 citation statements)

References 30 publications

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“…Yang et al [28] proposed Trident-LK Net, a three-layer network for steel surface defect detection that utilizes larger convolutional kernels for large objects and smaller kernels for small objects, enhancing detection granularity. Li et al [29] proposed a deep learning model for steel surface defect detection, featuring a multiscale feature extraction module that improves both feature extraction and fusion capabilities, thereby enhancing detection accuracy.…”

Section: Surface Defect Detection Methods Based On Cnnmentioning

confidence: 99%

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

Li,

Jiang,

Liang

2024

Meas. Sci. Technol.

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Industrial surface defect detection is an important part of industrial production, which aims to identify and detecting various defects on the surface of product to ensure quality and meet customer requirements. With the development of deep learning and image processing technologies, the surface defect detection methods based on computer vision has become the mainstream method. However, the prevalent convolutional neural network(CNN)-based de-fect detection methods also have many problems. For example, these methods rely on post-processing of Non-Maximum Suppression (NMS) and have poor detection ability for small targets, which affects the speed and accuracy of surface defect detection in industrial scenar-ios. Therefore, we propose a novel DEtection TRansformer (DETR)-based surface defect detection method. Firstly, we propose a Multi-scale Contextual Information Dilated module and fuse it into the backbone. The module is mainly composed of large kernel convolutions, which aims to expand the receptive field of the model, thus reducing the leakage rate of the model. Moreover, we design an efficient encoder which mainly contains two important mod-ules, namely feature enhancement based on cascaded group attention module and efficient feature fusion module based on content-aware. The former module effectively enhances the high-level semantic information extracted by the backbone, thus enabling the model to better interpret features, and it can improve the problem of high computational cost of transformer encoder, thus increasing the detection speed. The latter module performs multi-scale feature fusion across the feature information of various scales, thus improving the detection accura-cy of the model for small-size defects. Experimental results show that the proposed method achieves 80.6%mAP and 80.3FPS on NEU-DET, and 98.0%mAP and 79.4FPS on PCB-DET. Our proposed method exhibits excellent detection performance and achieves real-time and efficient surface defect detection capability to meet the needs of industrial surface defect detection.

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Section: Surface Defect Detection Methods Based On Cnnmentioning

confidence: 99%

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

Li,

Jiang,

Liang

2024

Meas. Sci. Technol.

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“…Additionally, large model sizes can pose deployment challenges on terminal devices. Precision (P), Recall (R), and Mean Average Precision (mAP) are commonly used as metrics to evaluate algorithm performance [4]. Furthermore, to evaluate the complexity and size of the model, we can consider the number of Floatingpoint Operations (FLOPs) and the number of parameters (Params).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…In industrial manufacturing, the production environment for steel is complex and susceptible to various factors such as temperature and impact [2]. This results in surface defects such as cracks, patches, scratches, and inclusions [3,4]. Steel surface defect detection algorithms are essential for ensuring product quality, steel safety, and controlling production costs.…”

Section: Introductionmentioning

confidence: 99%

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SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion

Li,

Yi,

Mei

et al. 2024

Processes

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The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy.

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“…Conducted in both zero-labeled sample (ZLS) and small-labeled sample (SLS) tasks, experiments show that the proposed method outperforms most state-of-the-art methods. Li et al [25] proposed a deep learning model based on a multi-scale feature extraction module for steel surface defects, deepening the backbone network by improving the Efficient Feature Fusion (EFF) and Bottleneck modules. The effectiveness of the designed module and method is verified on the public NEU-DET dataset.…”

Section: Introductionmentioning

confidence: 99%

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

Bu,

Yang,

et al. 2024

Coatings

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In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.

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A deep learning model for steel surface defect detection

Cited by 16 publications

References 30 publications

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

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