scholarly journals A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3348
Author(s):  
Zahid Ali Siddiqui ◽  
Unsang Park
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Real Time ◽  
Defect Detection ◽  
Power Line ◽  
Inspection Time ◽  
Inspection System ◽  
Continuous Exposure ◽  
Detection Algorithms ◽  
Detection And Localization

Defects in high voltage transmission line components such as cracked insulators, broken wires rope, and corroded power line joints, are very common due to continuous exposure of these components to harsh environmental conditions. Consequently, they pose a great threat to humans and the environment. This paper presents a real-time aerial power line inspection system that aims to detect power line components such as insulators (polymer and porcelain), splitters, damper-weights, power lines, and then analyze these transmission line components for potential defects. The proposed system employs a deep learning-based framework using Jetson TX2 embedded platform for the real-time detection and localization of these components from a live video captured by remote-controlled drone. The detected components are then analyzed using novel defect detection algorithms, presented in this paper. Results show that the proposed detection and localization system is robust against highly cluttered environment, while the proposed defect analyzer outperforms similar researches in terms of defect detection precision and recall. With the help of the proposed system automatic defect analyzing system, manual inspection time can be reduced.

Real-time on-site inspection system for power transmission based on heterogeneous computing

2021 ◽  
pp. 1-10
Author(s):  
Xiaohong Yan ◽  
Zhigang Zhao ◽  
Yongqiang Liu
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Power Supply ◽  
Heterogeneous Computing ◽  
Power Transmission ◽  
Modern Society ◽  
Power Line ◽  
Frame Rate ◽  
Inspection System ◽  
On Site Inspection

As the need of power supply is tremendously increasing in modern society, the stableness and reliability of the power delivery system are the two essential factors that ensure the power supply safety. With the quick expansion of electricity infrastructures, the failures of power transmission system are becoming more frequent, leading to economic loss and high risk of maintenance work under hazardous conditions. The existing automatic power line inspection utilizes advanced convolutional neural network (CNN) to improve the inspection efficiency, emerging as one promising solution. But the needed computational complexity is high since CNN inference demands large amount of multiplication-and-accumulation operations. In this paper, we alleviate this problem by utilizing the heterogeneous computing techniques to design a real-time on-site inspection system. Firstly, the required computational complexity of CNN inference is reduced using FFT-based convolution algorithms, speeding up the inference. Then we utilize the region of interest (ROI) extrapolation to predict the object detection bounding boxes without CNN inference, thus saving computing power. Finally, a heterogeneous computing architecture is presented to accommodate the requirements of proposed algorithms. According to the experiment results, the proposed design significantly improves the frame rate of CNN-based inspection visual system applied to power line inspection. The processing frame rate is also drastically improved. Moreover, the precision loss is negligible which means our proposed schemes are applicable for real application scenarios.

Prediction of Crime Scene Objects Using Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 857-864
Author(s):  
Vibhavari B Rao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Computer Architecture ◽  
Real Time ◽  
Surveillance System ◽  
Crime Scene ◽  
Detection Model ◽  
Detection Algorithms ◽  
Performance Metric ◽  
Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

scholarly journals Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 89278-89291 ◽  
Author(s):  
Jing Yang ◽  
Shaobo Li ◽  
Zheng Wang ◽  
Guanci Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Defect Detection ◽  
Detection System ◽  
Tiny Part

Fast and Parallel Summed Area Table for Fabric Defect Detection

2016 ◽  
Vol 30 (09) ◽  
pp. 1660004 ◽  
Author(s):  
Khaled Ragab
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Gpu Computing ◽  
Processing Unit ◽  
Detection Algorithms ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Time Requirements ◽  
Robustness To Noise ◽  
Graphical Processing

Automating fabric defect detection has a significant role in fabric industries. However, the existing fabric defect detection algorithms lack the real-time performance that is required in real applications due to their high demanding computation. To ensure real time, high accuracy and reliable fabric defect detection this paper developed a fast and parallel normalized cross-correlation algorithm based on summed-area table technique called PFDD-SAT. To meet real-time requirements, extensive use of the NVIDIA CUDA framework for Graphical Processing Unit (GPU) computing is made. The detailed implementation steps of the PFDD-SAT are illustrated in this paper. Several experiments have been carried out to evaluate the detection time and accuracy and then the robustness to illumination and Gaussian noises. The results show that the PFDD-SAT has robustness to noise and speeds the defect detection process more than 200 times than normal required time and that greatly met the needs for real-time automatic fabric defect detection.

scholarly journals REAL-TIME POWERLINE CORRIDOR INSPECTION BY EDGE COMPUTING OF UAV LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 547-551 ◽  
Author(s):  
S. Pu ◽  
L. Xie ◽  
M. Ji ◽  
Y. Zhao ◽  
W. Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Clouds ◽  
Power Line ◽  
Edge Computing ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Type I ◽  
Industry Standards ◽  
Potential Risks

<p><strong>Abstract.</strong> This paper presents an innovative power line corridor inspection approach using UAV LiDAR edge computing and 4G real real-time transmission. First, sample point clouds of power towers are manually classified and decomposed into components according to five mainstream tower types: T type, V type, n type, I type and owl head type. A deep learning AI agent, named “Tovos Age Agent” internally, is trained by supervised deep learning the sample data sets under a 3D CNN framework. Second, laser points of power line corridors are simultaneously classified into Ground, Vegetation, Tower, Cable, and Building types using semantic feature constraints during the UAV-borne LiDAR acquisition process, and then tower types are further recognized by Tovos Agent for strain span separation. Spatial and topological relations between Cable points and other types are analyzed according to industry standards to identify potential risks at the same time. Finally, all potential risks are organized as industry standard reports and transmitted onto central server via 4G data link, so that maintenance personal can be notified the risks as soon as possible. Tests on LiDAR data of 1000&amp;thinsp;KV power line show the promising results of the proposed method.</p>

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