scholarly journals Edge Intelligent Perception Method for Power Grid Icing Condition Based on Multi-Scale Feature Fusion Target Detection and Model Quantization

2021 ◽  
Vol 9 ◽  
Author(s):  
Fuqi Ma ◽  
Bo Wang ◽  
Min Li ◽  
Xuzhu Dong ◽  
Yifan Mao ◽  
...  
Keyword(s):  
Transmission Line ◽  
Target Detection ◽  
Power Transmission ◽  
Feature Fusion ◽  
Classical Method ◽  
Power Transmission Line ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Scale Feature ◽  
Multi Scale

Insulator is an important equipment of power transmission line. Insulator icing can seriously affect the stable operation of power transmission line. So insulator icing condition monitoring has great significance of the safety and stability of power system. Therefore, this paper proposes a lightweight intelligent recognition method of insulator icing thickness for front-end ice monitoring device. In this method, the residual network (ResNet) and feature pyramid network (FPN) are fused to construct a multi-scale feature extraction network framework, so that the shallow features and deep features are fused to reduce the information loss and improve the target detection accuracy. Then, the full convolution neural network (FCN) is used to classify and regress the iced insulator, so as to realize the high-precision identification of icing thickness. Finally, the proposed method is compressed by model quantization to reduce the size and parameters of the model for adapting the icing monitoring terminal with limited computing resources, and the performance of the method is verified and compared with other classical method on the edge intelligent chip.

scholarly journals Multi-Scale Feature Enhanced Domain Adaptive Object Detection For Power Transmission Line Inspection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 182105-182116
Author(s):  
Pengyu Zhang ◽  
Zhe Zhang ◽  
Yanpeng Hao ◽  
Zhiheng Zhou ◽  
Bing Luo ◽  
...  
Keyword(s):  
Object Detection ◽  
Transmission Line ◽  
Power Transmission ◽  
Power Transmission Line ◽  
Scale Feature ◽  
Multi Scale

scholarly journals MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1426
Author(s):  
Chuanyang Liu ◽  
Yiquan Wu ◽  
Jingjing Liu ◽  
Jiaming Han
Keyword(s):  
Feature Detection ◽  
Feature Fusion ◽  
Memory Storage ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Composite Insulator ◽  
Running Time ◽  
Scale Feature ◽  
Multi Scale ◽  
Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

scholarly journals CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection

2021 ◽  
Vol 13 (5) ◽  
pp. 847
Author(s):  
Wei Huang ◽  
Guanyi Li ◽  
Qiqiang Chen ◽  
Ming Ju ◽  
Jiantao Qu
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Target Detection ◽  
Feature Fusion ◽  
Detection Methods ◽  
Natural Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Backbone Network ◽  
Scale Detection

In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. Eventually, a focal loss in the prediction section is used to control the large number of negative samples generated during the feature fusion process. The new architecture of the network proposed in this paper is verified by DIOR and RSOD dataset. The experimental results show that the performance of this method is improved by 2–12% in the DIOR dataset and RSOD dataset compared with the current SOTA target detection methods.

scholarly journals Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 543 ◽  
Author(s):  
Haiyan Cheng ◽  
Yongjie Zhai ◽  
Rui Chen ◽  
Di Wang ◽  
Ze Dong ◽  
...  
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicles ◽  
Defect Detection ◽  
Power Transmission ◽  
Power Transmission Line ◽  
Detection Accuracy ◽  
Spatial Features ◽  
Color Features ◽  
Localization Precision ◽  
Good Detection

During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings in the inspection images, when the umbrella skirts of the rear string are obstructed by the front string, defect detection becomes difficult. To solve this problem, we propose a method to detect self-shattering defects of insulators based on spatial features contained in images. Firstly, the images are segmented according to the particular color features of glass insulators, and the main axes of insulator strings in the images are adjusted to the horizontal direction. Then, the connected regions of insulators in the images are marked. After that, the vertical lengths of the regions, the number of insulator pixels in the regions, as well as the horizontal distances between two adjacent connected regions are selected as spatial features, based on which defect discriminants are formulated. Finally, experiments are performed using the proposed formula to detect self-shattering defects in the insulators, using the spatial distribution of the connected regions to locate the defects. The experiment results indicate that the proposed method has good detection accuracy and localization precision.

scholarly journals An Image Multi-scale Feature Recognition Method Based on Image Saliency

2021 ◽  
Vol 15 ◽  
pp. 280-287
Author(s):  
Chengzhi Yang
Keyword(s):  
Image Recognition ◽  
Feature Recognition ◽  
Feature Fusion ◽  
Image Feature ◽  
Detection Accuracy ◽  
Recognition Method ◽  
Salient Region Detection ◽  
Adaptive Combination ◽  
Scale Feature ◽  
Multi Scale

Image recognition refers to the technology which processes, analyzes and understands images with computer so as to recognize various targets and objects of different patterns. To effectively combine image recognition and intelligent algorithm can enhance the efficiency of image feature analysis, improve the detection accuracy and guarantee real-time detection. In image feature recognition, the following problems exist: the description of accurate object features, object blockage, complex and changeable scenes. Whether these problems can be effectively solved has great significance in improving the stability and robustness of object recognition algorithm. This paper takes image salience as the fundamental framework, and makes in-depth study of the problems of effective object appearance description, multi-feature fusion and multi-feature adaptive combination. Then it proposes an image multi-scale feature recognition method based on image salience and it can better locate the saliency object in the image, and more evenly highlight the salient object and significantly suppress background noises. The experiment results prove that salient region detection algorithm can better stress the entire salient image.

scholarly journals Controllably Deep Supervision and Multi-Scale Feature Fusion Network for Cloud and Snow Detection Based on Medium- and High-Resolution Imagery Dataset

2021 ◽  
Vol 13 (23) ◽  
pp. 4805
Author(s):  
Guangbin Zhang ◽  
Xianjun Gao ◽  
Yuanwei Yang ◽  
Mingwei Wang ◽  
Shuhao Ran
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Feature Fusion ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Training Requirements ◽  
Scale Feature ◽  
Multi Scale ◽  
Hinge Loss

Clouds and snow in remote sensing imageries cover underlying surface information, reducing image availability. Moreover, they interact with each other, decreasing the cloud and snow detection accuracy. In this study, we propose a convolutional neural network for cloud and snow detection, named the cloud and snow detection network (CSD-Net). It incorporates the multi-scale feature fusion module (MFF) and the controllably deep supervision and feature fusion structure (CDSFF). MFF can capture and aggregate features at various scales, ensuring that the extracted high-level semantic features of clouds and snow are more distinctive. CDSFF can provide a deeply supervised mechanism with hinge loss and combine information from adjacent layers to gain more representative features. It ensures the gradient flow is more oriented and error-less, while retaining more effective information. Additionally, a high-resolution cloud and snow dataset based on WorldView2 (CSWV) was created and released. This dataset meets the training requirements of deep learning methods for clouds and snow in high-resolution remote sensing images. Based on the datasets with varied resolutions, CSD-Net is compared to eight state-of-the-art deep learning methods. The experiment results indicate that CSD-Net has an excellent detection accuracy and efficiency. Specifically, the mean intersection over the union (MIoU) of CSD-Net is the highest in the corresponding experiment. Furthermore, the number of parameters in our proposed network is just 7.61 million, which is the lowest of the tested methods. It only has 88.06 GFLOPs of floating point operations, which is less than the U-Net, DeepLabV3+, PSPNet, SegNet-Modified, MSCFF, and GeoInfoNet. Meanwhile, CSWV has a higher annotation quality since the same method can obtain a greater accuracy on it.

scholarly journals FE-RetinaNet: Small Target Detection with Parallel Multi-Scale Feature Enhancement

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 950
Author(s):  
Hong Liang ◽  
Junlong Yang ◽  
Mingwen Shao
Keyword(s):  
Target Detection ◽  
Semantic Information ◽  
Detection Accuracy ◽  
Top Down ◽  
Scale Feature ◽  
Feature Enhancement ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Small Targets ◽  
High Level

Because small targets have fewer pixels and carry fewer features, most target detection algorithms cannot effectively use the edge information and semantic information of small targets in the feature map, resulting in low detection accuracy, missed detections, and false detections from time to time. To solve the shortcoming of insufficient information features of small targets in the RetinaNet, this work introduces a parallel-assisted multi-scale feature enhancement module MFEM (Multi-scale Feature Enhancement Model), which uses dilated convolution with different expansion rates to avoid multiple down sampling. MFEM avoids information loss caused by multiple down sampling, and at the same time helps to assist shallow extraction of multi-scale context information. Additionally, this work adopts a backbone network improvement plan specifically designed for target detection tasks, which can effectively save small target information in high-level feature maps. The traditional top-down pyramid structure focuses on transferring high-level semantics from the top to the bottom, and the one-way information flow is not conducive to the detection of small targets. In this work, the auxiliary MFEM branch is combined with RetinaNet to construct a model with a bidirectional feature pyramid network, which can effectively integrate the strong semantic information of the high-level network and high-resolution information regarding the low level. The bidirectional feature pyramid network designed in this work is a symmetrical structure, including a top-down branch and a bottom-up branch, performs the transfer and fusion of strong semantic information and strong resolution information. To prove the effectiveness of the algorithm FE-RetinaNet (Feature Enhancement RetinaNet), this work conducts experiments on the MS COCO. Compared with the original RetinaNet, the improved RetinaNet has achieved a 1.8% improvement in the detection accuracy (mAP) on the MS COCO, and the COCO AP is 36.2%; FE-RetinaNet has a good detection effect on small targets, with APs increased by 3.2%.

Sign in / Sign up

Export Citation Format

Share Document