Abstract
The detection of the first wall tile and bolts (holes) of CFETR is crucial for improving the efficiency of remote operation maintenance. The internal environment of CFETR is complex, and the existing target detection algorithms lack precision, are prone to missed detection, and occupy substantial computational resources. We introduce a method for detecting tiles and bolts (holes) using a lightweight network. First, a preliminary research platform is established to build a dataset. Secondly, based on YOLOv5s, the GhostBottleNeck module replaces the BottleNeck in the C3 module to build a C3Ghost structure, reducing model computation and parameters. A fast spatial pooling pyramid structure, SimSPPF module, is proposed to replace the SPPF module, increasing model speed. The replacement of the CIOU loss function with EIOU enhances positional accuracy. Comparative experimental results indicate this network outperforms baseline YOLOv5s by increasing the mAP by 1.4 %, decreasing parameters by 13.8 %, and reducing floating-point operations (FLOPS) by 13.2 %. This study holds considerable reference value for future remote visual maintenance in CFETR.
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