Research on an Insulator Defect Detection Method Based on Improved YOLOv5

Qi, Yifan; Li, Yongming; Du, Anyu

doi:10.3390/app13095741

Cited by 8 publications

(4 citation statements)

References 30 publications

(45 reference statements)

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“…[15] achieved an even higher overall mAP of 97.82% and a detection speed of 43.2 fps. Finally, a method to enhance the feature extraction capability of the YOLOv5-small neural network was attempted in [16] to improve the detection performance while maintaining high speed. In [17], researchers achieved the highest results so far in detecting defective insulators, with the F1-score reaching 99.64%, using the latest YOLOv8 architecture combined with PS-ProtoPNet.…”

Section: Related Workmentioning

confidence: 99%

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Bellou,

Pisica,

Banitsas

2024

Energies

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The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8’s architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model’s pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5).

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Section: Related Workmentioning

confidence: 99%

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Bellou,

Pisica,

Banitsas

2024

Energies

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“…When integrated into the feature extraction module, this mechanism enhances the model's detection performance. It enables the extraction of fundamental location details from relevant defects, aiding tasks like identifying localized insulators 42 or traffic objects 43 . The introduction of this attention mechanism aims to enhance the network's ability to extract and represent image features, making it more expressive without increasing its depth.Figure 5 shows its channel attention structure.…”

Section: Normalization-based Attention Modulementioning

confidence: 99%

Steel Surface Defect Detection Based on Multi-Layer Fusion Networks

Li,

Liu,

Yin

et al. 2023

Preprint

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Detecting defects on steel surfaces is crucial for ensuring product quality and production safety in industrial settings. Object detection using deep learning, particularly the YOLOv5 model, has become a widely adopted method for this purpose. However, the complex shapes of current steel surface defects pose challenges for precise detection, especially when using low-cost recognition devices with small resolution images. To address these challenges, we integrated the RepBi-PAN fusion network into YOLOv5, enhancing the detection capability for large targets in complex backgrounds. To mitigate issues related to the premature introduction of shallow features and decrease in Precision, we optimized the model structure by incorporating the DenseNet structure into the backbone for improved feature extraction. Additionally, we introduced the Normalized Attention Module (NAM) to enhance the detection capability for small targets. Experimental results demonstrate the effectiveness of the enhanced model, showing a 4.1% increase in mean average precision (mAP), a 3.2% improvement in precision, and a 2.4% enhancement in recall. The improved algorithm outperforms the original in complex backgrounds and recognizing small targets, addressing limitations of the Rep-Bi network. Compared to other YOLO algorithms, our approach achieves optimal values for recall and mAP while maintaining a smaller model size. In comparison with YOLOv8, the improved model surpasses all V8 models, being only 0.5% below the precision of the largest YOLOv8x model. Simultaneously, the improved model is smaller and has fewer parameters compared to all models in the YOLOv8 series, with slightly higher GFLOPs than the smaller v8 models.

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“…Although many studies have addressed the insulator defect detection problem to some extent, some challenges remain [30]. For example, there are few original data scenarios and the inability to combine both detection accuracy and model size in a complex background [31].…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Detection Algorithm Based on YOLOv5s for Power Line Self-Exploding Insulators

Hu,

Min,

Liu

et al. 2023

Electronics

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In the process of inspecting the self-exploding defects of power line insulators, traditional algorithms suffer from various issues such as long detection time, insufficient accuracy, and difficulties in effective detection under complex environments. To address these problems, we introduce an advanced one-stage object detection algorithm called YOLOv5s, which offers fast training and excellent detection performance. In this paper, we applied the YOLOv5s algorithm to improve the detection precision and classification accuracy of insulator self-explosions. To further enhance the YOLOv5s algorithm, we introduced a BiFPN (Bidirectional Feature Pyramid Network) module for feature fusion. This module improved the feature fusion process by learning the importance weights of different input features, considering their contributions. To tackle the challenge of detecting small objects in the self-exploding insulator dataset, we incorporated an SPD (spatial-to-depth convolution) module that focuses on capturing features in small regions and utilizes one-step convolution layers to avoid losing fine-grained information. To address the issue of high similarity between self-exploding insulator regions and intact insulator regions, we introduced an attention mechanism that concentrates attention on the defective insulator regions to gather more information about insulator defects. Experimental results validate that all three improvement methods significantly enhance detection precision. The final model achieves improvements of 2.0% in precision, 0.9% in recall, and 1.5% in average detection accuracy. Through target detection of the test dataset, insulators with self-explosion cases can be effectively detected.

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Research on an Insulator Defect Detection Method Based on Improved YOLOv5

Cited by 8 publications

References 30 publications

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector

Steel Surface Defect Detection Based on Multi-Layer Fusion Networks

Research on an Improved Detection Algorithm Based on YOLOv5s for Power Line Self-Exploding Insulators

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