A Lightweight YOLOv4-EDAM Model for Accurate and Real-time Detection of Foreign Objects Suspended on Power Lines

Qiu, Zhibin; Zhu, Xuan; Liao, Chanjuan; Qu, Wenqian; Yu, Yanzhen

doi:10.1109/tpwrd.2022.3213598

Cited by 18 publications

(8 citation statements)

References 30 publications

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“…Once entangled in these lines, they pose a significant risk, potentially resulting in line failures and power interruptions. Furthermore, these studies 5 , 6 emphasize the broader threat posed by fluttering objects to power lines and suggest the need for relevant risk assessment methods. Fluttering objects, such as kites, tethered canvas and fabric materials, exhibit wind-induced vibrations and oscillations, increasing the likelihood of contact with power lines.…”

Section: Background and Summarymentioning

confidence: 99%

RailFOD23: A dataset for foreign object detection on railroad transmission lines

Chen,

Yang,

Feng

et al. 2024

Sci Data

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Artificial intelligence models play a crucial role in monitoring and maintaining railroad infrastructure by analyzing image data of foreign objects on power transmission lines. However, the availability of publicly accessible datasets for railroad foreign objects is limited, and the rarity of anomalies in railroad image data, combined with restricted data sharing, poses challenges for training effective foreign object detection models. In this paper, the aim is to present a new dataset of foreign objects on railroad transmission lines, and evaluating the overall performance of mainstream detection models in this context. Taking a unique approach and leveraging large-scale models such as ChatGPT (Chat Generative Pre-trained Transformer) and text-to-image generation models, we synthesize a series of foreign object data. The dataset includes 14,615 images with 40,541 annotated objects, covering four common foreign objects on railroad power transmission lines. Through empirical research on this dataset, we validate the performance of various baseline models in foreign object detection, providing valuable insights for the monitoring and maintenance of railroad facilities.

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Section: Background and Summarymentioning

confidence: 99%

RailFOD23: A dataset for foreign object detection on railroad transmission lines

Chen,

Yang,

Feng

et al. 2024

Sci Data

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“…This has suffered ups and downs in initial investments because market expectations for the Blockchain news media platform have not yet been high. Civil's principle is that professional journalists in various fields, including videos, write articles, and the first curator consists of about 200 professional journalists [45][46][47]. The news they produce requires objectivity and integrity to be verified by a committee and fact checker that thoroughly complies with the guidelines.…”

Section: A Issue Raisingmentioning

confidence: 99%

Artificial Intelligence Blockchain Based Fake News Discrimination

Kim,

Huh,

Kim

2024

IEEE Access

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This paper minimizes fake news, which has been a hot topic recently, using blockchain and artificial intelligence technology, and verifies it with blockchain. Also, using Artificial Intelligence technology, we want to create an algorithm that predicts how fake news will spread in the future. You can see various attempts at a news media platform based on Blockchain technology. However, the Blockchain news media platform is still not getting the market response we expected. It is questionable whether the reason is simply because it is a new technology, so it takes a long time to gain trust from consumers, whether consumers are not yet expecting an innovative news media platform, or whether the explosive growth of the Blockchain news media platform is difficult for other reasons. Research to answer this or direct research between Blockchain and media platforms is still lacking. In addition, the method of verifying fake news using artificial intelligence was verified, ANN, CBR, and MDA were changed, and the experiment was verified for progress. In addition, the use of 5-fold cross-validation as a comparative method was added as described above to more closely examine the possibility of its usefulness even in general situations. Also, through various fields of artificial intelligence and blockchain, verification work was done with blockchain, and fake news prediction was made using artificial intelligence. Various experiments were conducted and performance tests were performed, while the performance of about 5,000 TTPS was recorded through the third experiment. In the future, we think it is necessary to combine Artificial Intelligence and blockchain technology.

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“…Feng et al 14 utilized Mask R-CNN to identify the rails in different environments. Wu et al 15 proposed a diverse generation model to foreign objects intrusions. Alawad et al 16 proposed a framework that uses CNN to perform risk management in railway systems.…”

Section: /3mentioning

confidence: 99%

A Novel Fast Railway Foreign Objects Intrusion Detection Method Based on Cascaded Convolution Neural Network and Knowledge Distillation

Meng,

Chen,

Jiang

2024

Preprint

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The efficient and accurate detection of foreign objects invading railway tracks holds paramount importance in safeguarding the safety of train operations. Focusing on the problem of the low efficiency of the existing foreign objects detection methods, this work proposes a fast railway foreign objects intrusion detection method based on cascaded convolution neural network and knowledge distillation. First, a two-stage cascade convolution neural network is built.The first stage can identify whether the railway images are intruded by foreign objects or not. This is achieved by a light weight image classification network.In the second stage,YOLOv3 is employed to classify and locate the objects in the intruded railway image. The use of lightweight classification network can reduce the use of the object detection network, thus improving the overall efficiency of the railway foreign objects intrusion detection method in this paper. Secondly, this paper employs the Overhaul knowledge distillation algorithm to train a lightweight network that is supervised by a larger network, so that the lightweight network constructed in this paper also has satisfying image classification performance. Finally, the YOLOv3 object detection network is used to detect the foreign object image classified by the first level network. The experimental results demonstrate that the accuracy of the image classification network proposed in this paper is competitive to the classical backbone network, and the FPS is about 50–70 higher than the comparison method.

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A Lightweight YOLOv4-EDAM Model for Accurate and Real-time Detection of Foreign Objects Suspended on Power Lines

Cited by 18 publications

References 30 publications

RailFOD23: A dataset for foreign object detection on railroad transmission lines

RailFOD23: A dataset for foreign object detection on railroad transmission lines

Artificial Intelligence Blockchain Based Fake News Discrimination

A Novel Fast Railway Foreign Objects Intrusion Detection Method Based on Cascaded Convolution Neural Network and Knowledge Distillation

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