Improved YOLOv7 model for insulator defect detection

• URL: http://arxiv.org/abs/2502.07179v1
• Date: Tue, 11 Feb 2025 02:09:30 GMT
• Title: Improved YOLOv7 model for insulator defect detection
• Authors: Zhenyue Wang, Guowu Yuan, Hao Zhou, Yi Ma, Yutang Ma, Dong Chen,
• Abstract summary: This paper proposes an improved YOLOv7 model for multi-type insulator defect detection. The proposed model exhibits enhancements across various performance metrics.
• Score: 24.775038970508078
• License:
• Abstract: Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved YOLOv7 model for multi-type insulator defect detection. First, our model replaces the SPPCSPC module with the RFB module to enhance the network's feature extraction capability. Second, a CA mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a WIoU loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed.

Related papers

• Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m [19.080692737423693]
  This paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid.
  arXiv  Detail & Related papers  (2025-02-11T01:58:32Z)
• Improved YOLOv5s model for key components detection of power transmission lines [23.73288455723377]
  This paper proposes an improved object detection model based on the YOLOv5s (You Only Look Once Version 5 Small) model to improve the detection accuracy of key components of transmission lines. Our improved method's mAP (mean average precision) reached 98.1%, the precision reached 97.5%, the recall reached 94.4%, and the detection rate reached 84.8 FPS (frames per second)
  arXiv  Detail & Related papers  (2025-02-10T03:29:34Z)
• CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention [53.539020807256904]
  We introduce a Compact for Representations of Brain Oscillations using alternating attention (CEReBrO) Our tokenization scheme represents EEG signals at a per-channel patch. We propose an alternating attention mechanism that jointly models intra-channel temporal dynamics and inter-channel spatial correlations, achieving 2x speed improvement with 6x less memory required compared to standard self-attention.
  arXiv  Detail & Related papers  (2025-01-18T21:44:38Z)
• Robust Fine-tuning of Zero-shot Models via Variance Reduction [56.360865951192324]
  When fine-tuning zero-shot models, our desideratum is for the fine-tuned model to excel in both in-distribution (ID) and out-of-distribution (OOD) We propose a sample-wise ensembling technique that can simultaneously attain the best ID and OOD accuracy without the trade-offs.
  arXiv  Detail & Related papers  (2024-11-11T13:13:39Z)
• YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection [0.0]
  Existing detection methods for insulator defect identification from unmanned aerial vehicles struggle with complex background scenes and small objects. This paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second.
  arXiv  Detail & Related papers  (2024-10-15T16:00:01Z)
• An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators [0.0]
  Inspection of insulators is important to ensure reliable operation of the power system. Deep learning is being increasingly exploited to automate the inspection process. This article proposes the use of anomaly detection along with object detection in a two-stage approach for incipient fault detection.
  arXiv  Detail & Related papers  (2023-11-14T11:36:20Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)
• Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs [9.237363938772479]
  We perform semantic segmentation of defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images.
  arXiv  Detail & Related papers  (2021-10-15T17:57:59Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications [71.1911136637719]
  We show how provable guarantees can be naturally applied to other real world settings. We show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges.
  arXiv  Detail & Related papers  (2020-06-30T23:33:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.