Insulator Defect Detection Based on YOLOv5s-KE

The timely detection of insulator defects in transmission lines is vital for ensuring social production and people’s livelihoods. Aiming to solve the problem of the low accuracy of insulator defect detection in current detection models, this study improves the YOLO11n model and proposes an insulator defect detection model, FPFS-YOLO, that integrates FasterNet and an attention mechanism. In this study, to mitigate parameter redundancy in the backbone of the YOLO11n model, the FasterNet lightweight network was introduced, and some convolution was embedded into the shallow network to enhance its feature extraction ability. To solve problems such as insufficient attention to important features and the low detection ability of small defects in the YOLO11n model network, the ParNet attention mechanism was added, along with a small-defect detection layer, which improved the detection accuracy of the model. Finally, in order to alleviate the computational redundancy caused by these additions, the C3k2_faster module and the PSP-Head detection head were introduced. These amendments further improved the accuracy of the model network in detecting insulator defects while simultaneously reducing its computational redundancy. The experimental results show that the improved FPFS-YOLO model achieved a 91.5% mAP@50 and a 56.6% mAP@0.5-0.95, increases of 3.1% and 1.2%, respectively, while the precision and recall reached 93.2% and 86.4%, increases of 1.5% and 4.2%, respectively. The FPFS-YOLO model achieved a higher detection accuracy than the YOLO11n model and thus could be widely applied in the detection of insulator defects.

As power grid structure becomes increasingly complex, the impact of the operation status of key substation equipment on the safe and stable operation of the power grid is gradually increasing. Aiming at the problems of dense targets and occlusion recognition difficulties in the complex background of substations, an improved you-only-look-once (YOLO) v4 insulator defect detection algorithm is proposed. A large number of insulator and defect datasets were collected from the Internet. We use MobileNet-v2 to replace the backbone network of YOLOv4, and in view of the problem that the accuracy of YOLOv4 target detection model is not high for small targets or dense targets, convolutional block attention module (CBAM) attention mechanism is added to the backbone network to enhance the feature extraction ability, and ultra-lightweight subspace attention module (ULSAM) is added to the detection head to learn the cross-channel information. Experiments based on InDataset show that the insulator defect detection algorithm based on deep learning proposed achieves a high detection accuracy and detection speed; the mean average precision of the improved YOLOv4 model is 87.48%, and the recall rate of insulator defects reaches 79.84%, which meets the requirements of robustness and accuracy of insulator defect detection in real scenarios.

This paper proposes an innovative approach for detecting defects in insulators of overhead transmission lines. It employs a real-time object detector (RT-DETR) model named RESRTDETR to address the challenges faced by traditional methods in multi-object scenarios and complex backgrounds. The study addressed the issues of low accuracy and missed detections in traditional target detection algorithms for insulator defect detection. First, we propose the ResRepNet feature extraction network, which integrates the RepVGG reparameterized structure into existing backbone feature extraction networks to enhance the ability to extract features from defects of varying sizes. Secondly, since the information from neighboring channels surrounding insulator features plays a crucial role in small object detection, the ECA cross-channel attention mechanism is introduced. This leads to the proposal of the CA-CCFM module based on the CCFM module, enhancing the expressive capability of the head layer’s output features. Furthermore, to address the inherent limitations of the GIOU loss function in spatial localization, we introduce a SIoU loss function incorporating an angle penalty term to enhance the geometric awareness of bounding box regression. Finally, to enable the model to effectively learn features, a standard dataset comprising seven categories of samples was reconstructed to address the issues of imbalanced distribution and missing annotations in the CID insulator defect dataset. Data augmentation methods based on geometric transformations, color transformations, and mosaic stitching were employed to provide data support for subsequent experiments. Experiments validated the model’s effectiveness. Compared to existing object detection methods, RESRTDETR improves mAP@50 to 96.4% on the CID dataset and to 84.2% on the IDDD dataset. This approach not only enhances the accuracy of insulator defect detection but also plays a crucial role in maintaining the safety of power transmission systems, carrying far-reaching implications.

As a special insulation device, insulators play an important role in aerial transmission lines. It can be easily influenced by the wind load, snow load and wire swing in severe weather. The fault of the insulator will seriously affect and destroy the safety and normal operation of the entire transmission line. Therefore, in the operation and inspection of the transmission line, the detection of insulator defects must be strengthened. Since the insulators are constructed in a complex environment and the background of the images collected by air is also extremely complicated, traditional defect detection methods rely on specific scenarios for specific analysis and the robustness is weak. Changes in scenes, changes in UAV’s shooting angles, and changes in lighting conditions will all make it difficult for the model to get good inspection results. This paper proposes an algorithm for locating and detecting insulator defects based on an enhanced YOLO model. Due to the electromagnetic interference during the power inspection of the UAV, there is a certain amount of noise in the image of the aerial insulator. At the same time, in order to solve the problem of few shots which leads to training difficulty, this paper uses a variety of data augmentation, including affine transformation, Gaussian blur processing, gray scale transformation, brightness and contrast transformation and random erasing of pixels, and then the augmentation data is used to train different YOLO models. The results show that the detection algorithm based on the YOLO model meets the robustness and accuracy of insulator defect detection, and augmented experiment results are better than those of the conventional YOLOs in power line insulator defect detection tasks.

Printed circuit board PCB defect detection is crucial for ensuring PCB quality. However, small defect sizes on PCBs and the substantial parameter and computational requirements of deep learning models pose challenges in detection accuracy and deployment on resource-constrained devices. To address these issues, we introduce an optimized PCB defect detection method based on the RT-DETR model. Firstly, we propose the ContextAdown-D-LKAEfficientFormerV2 lightweight network to replace the ResNet18 backbone, reducing parameters and computations while enhancing small defect feature extraction. Secondly, we present the Bi-Slim-Neck lightweight structure to replace the CCFM component in the original model, achieving lightweight design and improved feature fusion capabilities to leverage effective features fully. Lastly, we propose the InnerShapeIoU loss function to replace the GIoU loss function, accounting for the influence of PCB defect bounding box shapes and scales on regression, and generating auxiliary bounding boxes suitable for PCB defect detection tasks and detectors. This enhances model generalization and detection accuracy. Experi mental results show that the improved model achieves detection accuracies of 97 mAP50 and 55 mAP50:95 , with a 23.8 reduction in parameters and a 44.9 decrease in computations compared to the original model. Thisindicates that the improved method significantly boosts parameter efficiency and reduces computational complexity while maintaining high detection accuracy.

Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing

Since insulators play an essential part in transmission lines, insulator defect detection could be an important assignments for intelligent inspection of high-voltage transmission lines. In this paper, an improved YOLOv5 algorithm for insulator defect detection task for aerial images with various backgrounds is proposed. We use collected aerial images of insulators with one or more defects in different scenarios, perform data augmentation of exposure and noise on the images to expand the sample (expanded to 2125 images), and establish a dataset by combining aerial images of normal working insulators. By adjusting the CSP(Cross-Stage-partial-connections) and CBL(Convalution-BatchNorm-Leaky_relu) modules in the YOLOv5 model to change the depth and width of the model, change model parameters, and build five different scale YOLOv5 models to further meet the real-time task. ResNet and DarkNet are used for the transfer learning of the YOLOv5 model, and various optimization methods are used in the Backbone structure, Neck structure and output of the model, then the established data set is trained and tested on each YOLOv5 model. Among them, the YOLOv5n model has the fastest detection speed, which can reach 10ms, and the precision also reaches 95%. The YOLOv5x model has the highest precision, reaching 97%, and the detection speed is 21ms. These models are all able to satisfy the accuracy and real-time mission in the process of aerial photography and analysis among which YOLOv5n can achieve lightweight tasks while being efficient enough.

Insulators are important components of transmission lines. Aiming at the problems of small detection targets and complex detection backgrounds in the images of insulators taken by drones in power inspections, this paper proposes an improved BM-YOLOv5 insulator defect detection method based on the YOLOv5 network. The multi-head self-attention structure is introduced to improve the performance of the model to capture long-distance dependencies, so that it can pay attention to global information; the original feature pyramid structure is replaced with a weighted bidirectional feature pyramid structure, and additional weights are added to features of different scales. Make full use of features between different scales. Based on the newly constructed insulator defect data set with complex background and small targets, the method is compared with the commonly used insulator defect detection models YOLOv5, YOLOv3, Faster-RCNN, and SSD. Compared with YOLOv5, the average accuracy has increased by 5.1%.

Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect detection network, LMD-YOLO, based on YOLOv8. The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. The SIoU loss function is used in place of CIoU to accelerate model convergence and improve detection accuracy. Additionally, this study creates a target detection dataset that encompasses four types of insulators: insulator, absent insulator, broken insulator, and shedding insulator. Experimental results show that LMD-YOLO achieves a 2% higher average accuracy on the insulator dataset compared to YOLOv8n, with a 24.6% reduction in model parameters, offering an effective solution for smart grid inspections.

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices.

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

Surface defects, which often occur during the production of aluminum profiles, can directly affect the quality of aluminum profiles, and should be monitored in real time. This paper proposes an effective, lightweight detection method for aluminum profiles to realize real-time surface defect detection with ensured detection accuracy. Based on the YOLOv5s framework, a lightweight network model is designed by adding the attention mechanism and depth-separable convolution for the detection of aluminum. The lightweight network model improves the limitations of the YOLOv5s framework regarding to its detection accuracy and detection speed. The backbone network GCANet is built based on the Ghost module, in which the Attention mechanism module is embedded in the AC3Ghost module. A compression of the backbone network is achieved, and more channel information is focused on. The model size is further reduced by compressing the Neck network using a deep separable convolution. The experimental results show that, compared to YOLOv5s, the proposed method improves the mAP by 1.76%, reduces the model size by 52.08%, and increases the detection speed by a factor of two. Furthermore, the detection speed can reach 17.4 FPS on Nvidia Jeston Nano’s edge test, which achieves real-time detection. It also provides the possibility of embedding devices for real-time industrial inspection.

Surface defect detection on smartphone screens is critical for ensuring product quality and user experience, but traditional methods often struggle to handle complex defect types and small-scale targets. This study proposes a novel algorithm based on YOLOv10m for detecting surface defects on smartphone screens. The detection capacity for small and medium-sized objects is improved by replacing the conventional Path Aggregation Network (PANet) with a Bidirectional Feature Pyramid Network (BiFPN). To significantly increase detection accuracy, the original loss function is replaced with the Normalized Wasserstein Distance (NWD) loss function. The enhanced algorithm achieves 59.4% precision, 41.6% recall, and 47.6% mAP50, according to experimental findings. Precision and mAP50 increased by 23.5% and 4.6%, respectively, over the initial YOLOv10m model. Interestingly, these gains were made without adding more parameters to the model, and the detection speed was greatly increased. Additionally, the suggested algorithm’s efficacy in identifying surface flaws on smartphone screens was compared to that of other prominent detection methods. In order to increase detection speed and accuracy, future research will concentrate on further streamlining the network architecture through the use of lightweight networks.

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds.