Improved YOLOv5s model for key components detection of power transmission lines

• URL: http://arxiv.org/abs/2502.06127v1
• Date: Mon, 10 Feb 2025 03:29:34 GMT
• Title: Improved YOLOv5s model for key components detection of power transmission lines
• Authors: Chen Chen, Guowu Yuan, Hao Zhou, Yi Ma,
• Abstract summary: 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)
• Score: 23.73288455723377
• License:
• Abstract: High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. Due to the low detection accuracy of key components in transmission line image inspection, this paper proposed 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. According to the characteristics of the power grid inspection image, we first modify the distance measurement in the k-means clustering to improve the anchor matching of the YOLOv5s model. Then, we add the convolutional block attention module (CBAM) attention mechanism to the backbone network to improve accuracy. Finally, we apply the focal loss function to reduce the impact of class imbalance. 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). The experimental results show that our improved model improves detection accuracy and has performance advantages over other models.

Related papers

• Research on vehicle detection based on improved YOLOv8 network [0.0]
  This paper proposes an improved YOLOv8 vehicle detection method. The improved model achieves 98.3%, 89.1% and 88.4% detection accuracy for car, Person and Motorcycle.
  arXiv  Detail & Related papers  (2024-12-31T06:19:26Z)
• 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)
• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network [9.775181958901326]
  We propose a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. The proposed method achieves a 92.8% accuracy rate, reduces the number of parameters by 27.78%, compresses computational volume by 41.25%, improves detection inference speed by 10.16%.
  arXiv  Detail & Related papers  (2023-06-21T08:55:45Z)
• A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head [0.0]
  Current state-of-the-art deep learning (DL)-based damage detection models often lack superior feature extraction capability in complex and noisy environments. DenseSPH-YOLOv5 is a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone. DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-07T22:53:36Z)
• Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore [71.09522172098733]
  We utilize graph representation in FSAD and provide a novel visual invariant feature (VIIF) as anomaly measurement feature. VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in M. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
  arXiv  Detail & Related papers  (2023-01-28T03:58:32Z)
• Improved YOLOv5 network for real-time multi-scale traffic sign detection [4.5598087061051755]
  We propose an improved feature pyramid model, named AF-FPN, which utilize the adaptive attention module (AAM) and feature enhancement module (FEM) to reduce the information loss in the process of feature map generation. We replace the original feature pyramid network in YOLOv5 with AF-FPN, which improves the detection performance for multi-scale targets of the YOLOv5 network.
  arXiv  Detail & Related papers  (2021-12-16T11:02:12Z)
• Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data [77.27338842609153]
  characterization reveals that sensitivity to each type of shift is important to the detection and confidence calibration of OOD data. We propose a geometrically-inspired method to improve OOD detection under both shifts with only in-distribution data. We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts.
  arXiv  Detail & Related papers  (2021-10-28T15:42:55Z)
• 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)
• Condensing Two-stage Detection with Automatic Object Key Part Discovery [87.1034745775229]
  Two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy. We propose that the model parameters of two-stage detection heads can be condensed and reduced by concentrating on object key parts. Our proposed technique consistently maintains original performance while waiving around 50% of the model parameters of common two-stage detection heads.
  arXiv  Detail & Related papers  (2020-06-10T01:20:47Z)

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.