An Efficient End-to-End Multitask Network Architecture for Defect Inspection
Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9845 |
| _version_ | 1827636987025162240 |
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| author | Chunguang Zhang Heqiu Yang Jun Ma Huayue Chen |
| author_facet | Chunguang Zhang Heqiu Yang Jun Ma Huayue Chen |
| author_sort | Chunguang Zhang |
| collection | DOAJ |
| description | Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process. |
| first_indexed | 2024-03-09T15:53:06Z |
| format | Article |
| id | doaj.art-fbb9c5aec09a4f448ac8ac9a42f40ef7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:53:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fbb9c5aec09a4f448ac8ac9a42f40ef72023-11-24T17:56:15ZengMDPI AGSensors1424-82202022-12-012224984510.3390/s22249845An Efficient End-to-End Multitask Network Architecture for Defect InspectionChunguang Zhang0Heqiu Yang1Jun Ma2Huayue Chen3School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Computer Science, China West Normal University, Nanchong 637002, ChinaRecently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process.https://www.mdpi.com/1424-8220/22/24/9845surface defect detectionsemantic segmentationobject detectionmulti-task network |
| spellingShingle | Chunguang Zhang Heqiu Yang Jun Ma Huayue Chen An Efficient End-to-End Multitask Network Architecture for Defect Inspection Sensors surface defect detection semantic segmentation object detection multi-task network |
| title | An Efficient End-to-End Multitask Network Architecture for Defect Inspection |
| title_full | An Efficient End-to-End Multitask Network Architecture for Defect Inspection |
| title_fullStr | An Efficient End-to-End Multitask Network Architecture for Defect Inspection |
| title_full_unstemmed | An Efficient End-to-End Multitask Network Architecture for Defect Inspection |
| title_short | An Efficient End-to-End Multitask Network Architecture for Defect Inspection |
| title_sort | efficient end to end multitask network architecture for defect inspection |
| topic | surface defect detection semantic segmentation object detection multi-task network |
| url | https://www.mdpi.com/1424-8220/22/24/9845 |
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