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- W4387130461 abstract "The visual detection of transmission lines is a key component to reduce the safety hazards in energy transmission tasks. However, due to the complex background of the defect target, the small size of the target, and the slight difference between the defect and normal bolts, detecting the lack of bolts and pins on transmission lines is a challenging problem to traditional methods. Inspired by strong feature learning ability of deep neural networks, we propose an efficient method based on fusion self-attention mechanism to actively address the issue of detecting bolts and pins on transmission lines. Specifically, the proposed method is designed to achieve classification and position location regression by fusing scale and spatial attention mechanisms in a unified manner. The extracted self-attention features are uniformly fused and utilized through the Fusion Attention Head. In order to address the problem of complex backgrounds for detecting pins, we propose a pin detection model by adding Fusion Attention Head which can uniformly fused and utilized the extracted self-attention features. Furthermore, we proposed a generalized Focal Loss is used to replace the traditional Focal Loss to achieve a joint representation of classification and quality estimation. Finally, we test the proposed model on a field-collected dataset of transmission line defects and compared it with the Faster R-CNN and YOLOv4. The experimental results demonstrate that the average accuracy is improved by 17%." @default.
- W4387130461 created "2023-09-29" @default.
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- W4387130461 date "2023-07-27" @default.
- W4387130461 modified "2023-10-18" @default.
- W4387130461 title "Self-Attention Mechanism based Visual Detection for Transmission Line Pins" @default.
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- W4387130461 doi "https://doi.org/10.1145/3613330.3613335" @default.
- W4387130461 hasPublicationYear "2023" @default.
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