Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion

Chen, Yifu; Liu, Hongye; Chen, Jiahao; Hu, Jun; Zheng, Enhui

doi:10.3390/electronics12153210

Cited by 23 publications

(5 citation statements)

References 31 publications

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“…Moreover, the background noise in the bearing surface defect dataset is significant, with a lot of useless interference information. To improve the sampling effect, this paper employs the CARAFE lightweight operator [21] to replace nearest-neighbor interpolation. CARAFE aggregates semantic information over a wide range, enhancing the quality of sampling in bearing images and reducing information loss.…”

Section: Carafementioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

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Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a Global Attention Mechanism (GAM) module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency.Subsequently, a deep deformable convolution feature pyramid network (TDSFPN) is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight Content-Aware Reassembly of Features upsampling operator (CARAFE) to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a Multi-Attention Detection Head (MADH) to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model's detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.

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Section: Carafementioning

confidence: 99%

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Ding,

Zhan,

et al. 2024

Meas. Sci. Technol.

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“…As a core technology in the domain of computer vision, object detection has gained broad utilization across various industrial sectors. Among them, the YOLO series algorithms have gradually become the preferred framework for most industrial applications due to their excellent overall performance [45][46][47][48][49]. However, in practical use, many algorithms fail to meet the requirements of industrial detection in terms of speed and accuracy.…”

Section: Yolov7 Algorithm Modelmentioning

confidence: 99%

High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning

Zhang

Zheng³

et al. 2023

Machines

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Computer vision technology is increasingly being widely applied in automated industrial production. However, the accuracy of workpiece detection is the bottleneck in the field of computer vision detection technology. Herein, a new object detection and classification deep learning algorithm called CSW-Yolov7 is proposed based on the improvement of the Yolov7 deep learning network. Firstly, the CotNet Transformer structure was combined to guide the learning of dynamic attention matrices and enhance visual representation capabilities. Afterwards, the parameter-free attention mechanism SimAM was introduced, effectively enhancing the detection accuracy without increasing computational complexity. Finally, using WIoUv3 as the loss function effectively mitigated many negative influences during training, thereby improving the model’s accuracy faster. The experimental results manifested that the mAP@0.5 of CSW-Yolov7 reached 93.3%, outperforming other models. Further, this study also designed a polyhedral metal workpiece detection system. A large number of experiments were conducted in this system to verify the effectiveness and robustness of the proposed algorithm.

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“…PConv has lower computational effort as well as higher computational efficiency, which can utilize the computational power of the device more efficiently and also improves the model's ability to extract spatial feature information. Based on this, Chen Y et al (2023) proposed FasterNet, which can achieve better results in classification, detection and segmentation tasks at a faster rate, and its can replace the Backbone part of the YOLOv5 model.…”

Section: Lightweight Network Architecturementioning

confidence: 99%

“…Zhang et al (2023) used GhostNet as the Backbone network of the YOLOv4 model, and at the same time optimized the model using K-means algorithm and Focal loss function. Chen Y et al (2023) added the GSConv module to the latest YOLOv8n algorithm to reduce the complexity of the network, and also adopted a lightweight Content-Aware Feature Reconstruction (CARAFE) structure to enhance the feature fusion capability of the model. Miao et al (2019) used a combination of SSD model and two-stage fine-tuning strategy to complete the detection of defective insulators, which can automatically extract multi-level features of images and can identify porcelain insulators and composite insulators quickly and accurately in complex backgrounds.…”

Section: Introductionmentioning

confidence: 99%

The application of a lightweight model FA-YOLOv5 with fused attention mechanism in insulator defect detection

Liu,

Hu,

Dong

et al. 2023

Front. Energy Res.

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Insulators are important components of transmission lines, serving as support for conductors and preventing current backflow. However, insulators exposed to natural environments for a long time are prone to failure and can cause huge economic losses. This article proposes a fast and accurate lightweight Fast and Accurate YOLOv5s (FA-YOLO) model based on YOLOv5s model. Firstly, attention mechanisms are integrated into the network module, improving the model’s ability to extract and fuse target features. Secondly, the backbone part of the network is lightweightened to reduce the number of parameters and computations at the cost of slightly reducing the accuracy of detecting a few objects. Finally, the loss function of the model is improved to accelerate the convergence of the network and improve detection accuracy. At the same time, a visual insulator detection interface is designed using PyQt5. The experimental results show that the algorithm in this paper reduces the number of parameters by 28.6%, the computational effort by 35.7%, and the mAP value by 1.7% compared with the original algorithm, and is able to identify defective insulators quickly and accurately in complex backgrounds.

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Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion

Cited by 23 publications

References 31 publications

A bearing surface defect detection method based on multi-attention mechanism Yolov8

A bearing surface defect detection method based on multi-attention mechanism Yolov8

High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning

The application of a lightweight model FA-YOLOv5 with fused attention mechanism in insulator defect detection

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