An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5

Chen, Kun; Li, Hongtao; Li, Chunshu; Zhao, Xinyue; Wu, Shujie; Duan, Yuxiao; Wang, Jinshen

doi:10.3390/s22207907

Cited by 38 publications

(16 citation statements)

References 51 publications

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“…According to the characteristics of remote sensing images, Wang et al [27] adopted a circular smooth label (CSL) method to calculate the loss of the rotating object detection bounding box and used the FcaNet attention mechanism to design new feature fusion modules. Chen et al [28] combined Cycle-GAN and YOLOv5 to detect petrochemical pipelines, improving the efficiency and accuracy of detection.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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Magnetic rings are widely used in automotive, home appliances, and consumer electronics. Due to the materials used, processing techniques, and other factors, there will be top cracks, internal cracks, adhesion, and other defects on individual magnetic rings during the manufacturing process. To find such defects, the most sophisticated YOLOv5 target identification algorithm is frequently utilized. However, it has problems such as high computation, sluggish detection, and a large model size. This work suggests an enhanced lightweight YOLOv5 (MR-YOLO) approach for the identification of magnetic ring surface defects to address these issues. To decrease the floating-point operation (FLOP) in the feature channel fusion process and enhance the performance of feature expression, the YOLOv5 neck network was added to the Mobilenetv3 module. To improve the robustness of the algorithm, a Mosaic data enhancement technique was applied. Moreover, in order to increase the network’s interest in minor defects, the SE attention module is inserted into the backbone network to replace the SPPF module with substantially more calculations. Finally, to further increase the new network’s accuracy and training speed, we substituted the original CIoU-Ioss for SIoU-Loss. According to the test, the FLOP and Params of the modified network model decreased by 59.4% and 47.9%, respectively; the reasoning speed increased by 16.6%, the model’s size decreased by 48.1%, and the mAP only lost by 0.3%. The effectiveness and superiority of this method are proved by an analysis and comparison of examples.

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Section: Deep Learning Methodsmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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“…These methods are employed in many fields, including industry, medicine, and mapping. For example, Chen et al [ 10 ] used Cycle-GAN to generate petrochemical pipeline defect images and expanded their dataset, achieving an average accuracy of 93.10% by training the target detection model. Salehinejad et al [ 11 ] used DCGAN to augment chest radiographs and used a combination of real and generated images to train a deep convolutional neural network to achieve pathological detection of five different types of chest radiographs.…”

Section: Related Workmentioning

confidence: 99%

Waste Detection System Based on Data Augmentation and YOLO_EC

Fan

Cui

Fei

2023

Sensors

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The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection.

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“…At the same time, the safety helmet worn by the workers in the picture was separately labeled as "helmet" [9]. The logo of the State Grid on the safety helmet was labeled as "logo" [10]. The YOLOv7 network model has trained again for 100 rounds.…”

Section: Distinguish Workers By Other Characteristicsmentioning

confidence: 99%

Research on Human Behavior Feature Recognition and Intelligent Early Warning Methods in Safety Supervision Scene Video based on Yolov7

Cao

Gong

et al. 2023

J. Phys.: Conf. Ser.

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With the continuous improvement of infrastructure construction in China, the construction of various supporting power facilities is constantly followed up. A large number of ultra-high voltage or high-voltage power facilities, such as high-voltage transmission towers, transformers, or substations, are gradually emerging in populated areas, farmland, or fishponds where people still pass by sometimes. In such circumstances, people who do not have the qualification to contact power facilities at times approach, contact, or even climb these dangerous high-voltage transmission and distribution facilities illegally for various reasons, which not only poses a huge hidden peril or even threat to the safety of people’s lives and property but also cause a huge negative impact on the smooth operation of power infrastructure. To solve this intractable problem, this topic proposes a pedestrian detection and recognition algorithm, which can accurately distinguish between ordinary pedestrians and power grid maintenance inspectors based on YOLOv7, providing a reliable method for timely detection and early warning of pedestrians who may contact power facilities.

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An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5

Cited by 38 publications

References 51 publications

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Waste Detection System Based on Data Augmentation and YOLO_EC

Research on Human Behavior Feature Recognition and Intelligent Early Warning Methods in Safety Supervision Scene Video based on Yolov7

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