Using Convolution Neural Networks to Build a LightWeight Anomalies Detection Model

Luo, Wang; Hao, Yunhe; Xu, Huarong; Wu, Jiankang; Ju, Xiaoming

doi:10.1109/auteee52864.2021.9668683

Cited by 1 publication

(4 citation statements)

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“…In order to solve the problem of imbalanced sample distribution, we used two different methods to construct two new datasets. Firstly, we filtered and then removed some images with less obvious features from the negative samples to get a small dataset which we named SMALL [21]. In this dataset, the negative sample was removed to only 282 images, and the positive sample was 270 images to reach a balanced sample.…”

Section: Datasetsmentioning

confidence: 99%

“…Secondly, we replicate the 270 negative samples of the original data 6 times to reach 1620. This results in a balanced set of 1616 positive samples, which is called LARGE [21]. The original dataset is named MIDDLE [21].…”

Section: Datasetsmentioning

confidence: 99%

“…This results in a balanced set of 1616 positive samples, which is called LARGE [21]. The original dataset is named MIDDLE [21]. The specific data set is shown in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

“…In our experiments we divide the datasets into training set, validation set and test set in the ratio of 8:1:1, respectively. We train the model on the training set, tune the parameters by the validation set, and finally test the model on the test set [21].…”

Section: Datasetsmentioning

confidence: 99%

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CTran_DA: Combine CNN with Transformer to Detect Anomalies in Transmission Equipment Images

Hong-hui¹,

Qin²,

Wu³

et al. 2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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With the development of the State Grid, the power lines, equipment and transmission scale are expanding. In order to ensure the stability and safety of electricity, it is necessary to patrol and inspect the power towers and other equipment. With the help of deep learning, neural networks can be used to learn the features in patrol image. In this paper, feature learning model named CNN Transformer Detect Anomalies (CTran_DA) is proposed to detect anomalies in patrol images. CTran_DA uses CNN to learn local features in the image, and uses Transformer to learn global features. This paper innovatively combines the advantages of CNN and Transformer to learn the local details as well as the global feature associations in images. By comparing experiments on out self-constructed dataset, the model outperforms state-of-the-art baselines. Moreover, the Floating Point Operations (FLOPs) and parameters of the model in this paper are smaller than other algorithms. In general, CTran_DA is an efficient and lightweight model to detect anomalies in images.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

“…This results in a balanced set of 1616 positive samples, which is called LARGE [21]. The original dataset is named MIDDLE [21]. The specific data set is shown in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations