Transmission Line Detection Through Bi-Directional Guided Registration With Knowledge Distillation

Transmission line (TL) inspection plays a crucial role in maintaining a reliable electricity supply to all regions. Computer vision methods, especially those utilizing infrared images, have achieved significant advancements in this field. However, many existing multimodal fusion methods utilize conventional attention mechanisms or simple meta-additions to combine different modalities without proper alignment. Moreover, these methods often rely on a large number of parameters to achieve better performance. To enhance the fusion of disparate modalities and minimize model parameters, we propose bidirectional guided registration via knowledge distillation (BGRNet-S
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) for RGB-T transmission line detection (TLD). This approach incorporates a bidirectional registration mechanism within the fusion module and achieves parameter reduction through our knowledge distillation (KD) method. Based on the bidirectional guidance of the non-local position encoding module, accurate feature registration between modes can be achieved. Additionally, we designed response distillation and spatial semantic distillation for our student network (BGRNet-S). Extensive experiments on TLD datasets demonstrate that both our BGRNet-T and BGRNet-S
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(BGRNet-S with KD) achieve excellent performance using state-of-the-art methods. When using Shunted-B and Shunted-T as the backbones of the BGRNet-T and BGRNet-S
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networks, respectively, the number of parameters was reduced from 52.34M to 13.09M, and the calculated floating-point numbers decreased from 25.23G to 8.93G.