Infrared Image Recognition of Substation Equipment Based on Adaptive Feature Fusion and Attention Mechanism

WANG Yuanbin; WU Bingchao

doi:10.11999/JEIT231047

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WANG Yuanbin, WU Bingchao. Infrared Image Recognition of Substation Equipment Based on Adaptive Feature Fusion and Attention Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231047

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WANG Yuanbin, WU Bingchao. Infrared Image Recognition of Substation Equipment Based on Adaptive Feature Fusion and Attention Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231047

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Infrared Image Recognition of Substation Equipment Based on Adaptive Feature Fusion and Attention Mechanism

doi: 10.11999/JEIT231047

WANG Yuanbin^,,
WU Bingchao

1.
College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
2.
Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi’an 710054, China

Funds: The National Natural Science Foundation of China (52174198), The National Key Research and Development Program of Shaanxi Province (2023 YBSF-133), The Construction of Qinchuangyuan Scientists and Engineers Team in Shaanxi Province (2022KXJ-166)

Received Date: 2023-09-27
Rev Recd Date: 2023-12-03

Available Online: 2023-12-14

Abstract

Abstract

To address the challenges of poor recognition effect of the infrared substation equipment image caused by multi-target, small target and occlusion target in complex background, an infrared image recognition method for substation equipment based on CenterNet is proposed. By combining the Adaptive Spatial Feature Fusion(ASFF) module and Feature Pyramid Networks (FPN), a feature fusion network with the structure of ASFF+FPN is constructed, and the cross-scale feature fusion capability of the model for multi-target and small target is enhanced, which excludes background information. To improve the feature capturing ability of occluding targets, the global attention mechanism is introduced to the feature fusion network to enhance target saliency. Additionally, depthwise separable convolution is introduced to reduce parameters number and model inference time, and a lightweight model is achieved. Finally, the problem of poor sensitivity to obscured targets is overcame by using the distribution focal loss function, and the convergence speed and recognition accuracy is improved. Tests are performed on a self-built dataset containing seven infrared substation equipment images. Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 95.19%, an improvement of 3.55% compared with the original algorithm, while it only has 32.52M model parameters. Furthermore, the method shows significant advantages in recognition accuracy and algorithm complexity, over four main target recognition algorithms.
- Substation equipment,
- Infrared image recognition,
- CenterNet,
- Adaptive Spatial Feature Fusion (ASFF),
- Attention mechanism.

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References(19)

References

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