A Lightweight Algorithm for Insulator Target Detection and Defect Identification

Han, Gujing; Zhao, Liang; Li, Qiang; Li, Saidian; Wang, Ruijie; Yuan, Qiwei; He, Min; Yang, Shiqi; Qin, Liang

doi:10.3390/s23031216

Cited by 13 publications

(13 citation statements)

References 33 publications

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“…On the basis of focal loss, the paper fuses SIOU loss for boundary regression. Defining the fused boundary regression loss function focal-SIOU-loss as follows: LFocal_SIOU=FLγLSIOU,FL(pt)=−αt(1−pt)γ log(pt),where FL is the focal loss, γ is a super parameter used to control curve radian, αnormalt weight helps to deal with the imbalance of categories, and (1−ptfalse)γ is a regulating factor, LSIOU loss function used for boundary regression, compared with CIOU, 31 DIOU, GIOU, 32 and EIOU, 33 SIOU not only considers the length and width information of the prediction box but also considers the matching angle direction. The prediction box returns to the nearest x-axis or y-axis faster.…”

Section: Cn-yolo Network Structurementioning

confidence: 99%

“…where FL is the focal loss, γ is a super parameter used to control curve radian, α t weight helps to deal with the imbalance of categories, and ð1 − p t Þ γ is a regulating factor, L SIOU loss function used for boundary regression, compared with CIOU, 31 DIOU, GIOU, 32 and EIOU, 33 SIOU not only considers the length and width information of the prediction box but also considers the matching angle direction. The prediction box returns to the nearest x-axis or y-axis faster.…”

Section: Focal-siou-loss For Model Convergencementioning

confidence: 99%

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CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

Liu,

2024

J. Electron. Imag.

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Quick and accurate detection of insulator defects from the complex aerial background (such as trees, hillsides, lakes, and buildings) is important work to ensure the safe operation of transmission lines. The existing detection methods have difficulty detecting the defect target due to the strong interference of complex backgrounds in aerial images. To solve this problem, we propose an insulator defect detection model based on a cascaded network. First, we introduce a hierarchical semantic segmentation network to separate the complex background from the target insulator, which is embedded into the main feature extraction branch to form a "segmentation-detection" cascade network to solve the interference problem of complex background when extracting target information; Second, aiming at the problem of direct fusion of conflicting information in different feature layers in the bi-directional path aggregation neck structure in the detection network, we propose an acrossscale feature pyramid with feature refinement structure to enhance the information characteristics of insulator defect targets and improve the multi-scale expression ability of the network. Then, aiming at the problem of difficult samples and imbalance of positive and negative samples in the process of target detection, we propose a focal shape intersection over union loss (focal-SIOU-loss), which improves the precision and stability of the regression process by introducing the weight adjustment mechanism of focal loss and the structural similarity of SIOU Loss. Finally, the experimental results show that, compared with the standard detection models such as YOLOv5, YOLO7, and YOLOv8, the proposed detection model achieves a better performance in the precision, recall rate, and robustness in detecting insulator defects under complex backgrounds.

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Section: Cn-yolo Network Structurementioning

confidence: 99%

Section: Focal-siou-loss For Model Convergencementioning

confidence: 99%

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

Liu,

2024

J. Electron. Imag.

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“…Unlike a plain network with only a single path of input to output, the residual network is a collection of many paths; therefore, removing certain paths or layers still leaves the other half path valid [ 51 ]. In addition to pruning techniques, in some studies, the backbones of the YOLO models are replaced with lighter convolutional networks, such as EfficientNet [ 52 ], MobileNetv3 [ 53 ], GhostNet [ 54 ] and DenseNet [ 55 ]. Previous research has shown that channel and layer pruning are mainly done to realise more feasible automatic detections.…”

Section: Introductionmentioning

confidence: 99%

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Sukumarran,

Hasikin,

Khairuddin

et al. 2024

Parasites Vectors

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Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

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“…Therefore, as a low-cost, short-cycle, and highly maneuverable inspection method, drones are receiving increasing attention in power transmission line inspection [ 8 , 9 , 10 ]. By processing the images of transmission lines obtained through unmanned aerial vehicle (UAV) inspection, it is possible to accurately detect hidden dangers in transmission lines, reduce the incidence of accidents, and improve the reliability of the power grid operating environment [ 11 , 12 , 13 ]. Processing and analyzing images captured by UAVs can effectively detect the positions of electrical components and provide technical support for autonomous inspections of transmission line components using UAVs, the autonomous navigation of UAVs, the automatic focusing of cameras, and defect identification.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

Liu,

Huang

2024

Sensors

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Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

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A Lightweight Algorithm for Insulator Target Detection and Defect Identification

Cited by 13 publications

References 33 publications

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

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