ISSD: Improved SSD for Insulator and Spacer Online Detection Based on UAV System

Li, Xuan; Li, Yong; Shuang, Feng; Gao, Fuqing; Zhou, Xiang; Chen, Xingzhi

doi:10.3390/s20236961

Cited by 38 publications

(18 citation statements)

References 23 publications

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“…This paper describes a system of judging whether people are wearing masks that has been improved and optimized based on the SSD algorithm. The SSD algorithm combines the anchor mechanism of faster R-CNN and the regression idea of YoLo, and improves the speed and accuracy [40][41][42]. The multi-scale convolution feature map was used to predict the object region, and a series of discrete and multi-scale default frame coordinates were output.…”

Section: Face Mask Detection Modulementioning

confidence: 99%

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Fan

Yang

et al. 2021

Drones

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Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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Section: Face Mask Detection Modulementioning

confidence: 99%

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Fan

Yang

et al. 2021

Drones

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“…For example, Zhang et al used the SIFT operator and the Euclidean distance function to determine whether the insulator has defects [6]. The deep learning methods adopt deep neural networks to automatically learn the image features, and optimizes network parameters by training large-scale data to improve object detection accuracy, i.e., Faster R-CNN [7], SSD [8], YOLO algorithms [9] and so on. Wang et al [7] used the Faster R-CNN to quickly classify and locate insulators, and determine whether the insulators self-detonate through the classification network.…”

Section: Introductionmentioning

confidence: 99%

Insulator recognition and self-explosion defect detection method based on improved YOLOv5 model

Li,

Du,

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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The insulator images taken by intelligent unmanned inspection equipment often have many small insulators in largeresolution scenes, and sparsely distributed self-destruct defects with complex backgrounds. These phenomena have brought great challenges to the efficient and accurate insulator recognition and defect detection based on insulator images. Therefore, this paper builds a large insulator image database, and proposes an insulator detection method through improving the YOLOv5 model by adding the smaller detection layer and optimizing the loss function to accurately classify and identify small insulators and detect their self-explosion defects. The proposed method can achieve a recognition accuracy rate of 97.8%, a recall rate of 96.2%, and a self-explosion defect detection accuracy rate of 95.1% in the constructed insulator database. The experimental results demonstrate that the proposed model has outstanding performance in terms of insulator recognition and the self-explosion defect detection, which can improve the efficiency of intelligent inspection of power grid system in practical application.

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“…For example, cascade of the regions with cnn features (Cascade RCNN) [15], single shot multi-box detector (SSD) [16], RetinaNet [17], Mask RCNN [18], you only look once (YOLO) [19] and other methods. Liu et al [20] proposed an improved SSD insulator-detection algorithm, using a lightweight network MnasNet [21] as a feature extraction network, and then using a multi-scale fusion method to fuse the feature maps. The author used the dataset of aerial images to conduct experiments.…”

Section: Introductionmentioning

confidence: 99%

ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

Zhou¹,

Wang²,

Li³

2022

Sensors

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Traditional power equipment defect-detection relies on manual verification, which places a high demand on the verifier’s experience, as well as a high workload and low efficiency, which can lead to false detection and missed detection. The Mask of the regions with CNN features (Mask RCNN) deep learning model is used to provide a defect-detection approach based on the Mask RCNN of Attention, Rotation, Genetic algorithm (ARG-Mask RCNN), which employs infrared imaging as the data source to assess the features of damaged insulators. For the backbone network of Mask RCNN, the structure of Residual Network 101 (ResNet101) is improved and the attention mechanism is added, which makes the model more alert to small targets and can quickly identify the location of small targets, improve the loss function, integrate the rotation mechanism into the loss function formula, and generate an anchor frame where a rotation angle is used to accurately locate the fault location. The initial hyperparameters of the network are improved, and the Genetic Algorithm Combined with Gradient Descent (GA-GD) algorithm is used to optimize the model hyperparameters, so that the model training results are as close to the global best as possible. The experimental results show that the average accuracy of the insulator fault-detection method proposed in this paper is as high as 98%, and the number of frames per second (FPS) is 5.75, which provides a guarantee of the safe, stable, and reliable operation of our country’s power system.

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ISSD: Improved SSD for Insulator and Spacer Online Detection Based on UAV System

Cited by 38 publications

References 23 publications

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Insulator recognition and self-explosion defect detection method based on improved YOLOv5 model

ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN

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