Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable...Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.展开更多
To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a li...To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a lightweight algorithm based on YOLOv11(You Only Look Once version 11).The algorithm presents efficient downsampling module,new feature extraction module and innovative neck structure.By integrating the spatial channel attention module of frequency-aware cascade attention(FCA)and the ADown module,the number of parameters is reduced while accuracy is significantly improved.Additionally,the neck module is redesigned,and the position-aware key feature fusion network(PKFN)module is introduced to further improve feature fusion capabilities.Experiments were conducted on the SAID dataset using the improved model.Compared to the original model,the m AP(0.5)of ADFP shows a 5.3%improvement,while the model parameters are reduced by 12.0%.On other public insulator component defect datasets,these improvements still have better results.Multiple experiments have confirmed the effectiveness of the model and its strong generalization ability.展开更多
Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the sca...Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the scarcity of comprehensive,high-quality datasets for insulator defects.To address this gap,the synthetic insulator defect imaging and annotation(SYNTHIDIA)system was proposed.SYNTHIDIA generates synthetic defect images in a 3D virtual environment using domain randomisation,offering a cost-effective and versatile solution for creating diverse and annotated data.Our dataset includes 22,000 images with accurate pixel-level and instance-level annotations,covering broken defect and drop defect types.Through rigorous experiments,SYNTHIDIA demonstrates strong generalisation capabilities to real-world data and provides valuable insights into the impact of various domain factors on model performance.The inclusion of 3D models further supports broader research initiatives.SYNTHIDIA addresses data insufficiency in insulator defect detection and enhances model performance in data-limited scenarios,contributing significantly to the advancement of power inspection.展开更多
基金State Grid Jiangsu Electric Power Co.,Ltd.of the Science and Technology Project(Grant No.J2022004).
文摘Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.
基金Supported by the Natural Science Foundution of Heilongjiang Province(LH2024E109)。
文摘To address the challenges of high model complexity and low accuracy in insulator component defect detection from drone-captured images,this paper presents adaptive downsampling and frequency-position fusion(ADFP),a lightweight algorithm based on YOLOv11(You Only Look Once version 11).The algorithm presents efficient downsampling module,new feature extraction module and innovative neck structure.By integrating the spatial channel attention module of frequency-aware cascade attention(FCA)and the ADown module,the number of parameters is reduced while accuracy is significantly improved.Additionally,the neck module is redesigned,and the position-aware key feature fusion network(PKFN)module is introduced to further improve feature fusion capabilities.Experiments were conducted on the SAID dataset using the improved model.Compared to the original model,the m AP(0.5)of ADFP shows a 5.3%improvement,while the model parameters are reduced by 12.0%.On other public insulator component defect datasets,these improvements still have better results.Multiple experiments have confirmed the effectiveness of the model and its strong generalization ability.
基金supported by Guangdong Power Grid Co.Ltd.Science and Technology Project(GDKJXM20231455).
文摘Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the scarcity of comprehensive,high-quality datasets for insulator defects.To address this gap,the synthetic insulator defect imaging and annotation(SYNTHIDIA)system was proposed.SYNTHIDIA generates synthetic defect images in a 3D virtual environment using domain randomisation,offering a cost-effective and versatile solution for creating diverse and annotated data.Our dataset includes 22,000 images with accurate pixel-level and instance-level annotations,covering broken defect and drop defect types.Through rigorous experiments,SYNTHIDIA demonstrates strong generalisation capabilities to real-world data and provides valuable insights into the impact of various domain factors on model performance.The inclusion of 3D models further supports broader research initiatives.SYNTHIDIA addresses data insufficiency in insulator defect detection and enhances model performance in data-limited scenarios,contributing significantly to the advancement of power inspection.
