A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines

Checking the distance between aircraft fire detection lines is a crucial task in the conformity inspection process of civil aircraft manufacturing. Currently, this task is mainly performed manually, which is inefficient and prone to errors and omissions. To address this issue, we propose a method for detecting the distance between aircraft fire detection lines based on the Segment Anything Model (SAM). In this method, we develop a general model for aircraft parts image segmentation and detection, named the Aircraft Segment Anything Model (ASAM). This model uses a low-rank fine-tuning strategy (LoRA) to fine-tune the encoder and mask decoder synchronously and incorporates a fusion loss function for adaptive training of the target task. The trained ASAM model is tested on a self-built fire detection line segmentation dataset, achieving Dice scores of 85.28, 85.99, and 86.26 based on sam_vit_b, sam_vit_l, and sam_vit_h weights, respectively. It scores 13.82 points higher than the classic segmentation model K-Net under the same training parameters. The proposed method provides a new approach for the widespread application of SAM in the field of aircraft parts image segmentation.