FMSD: Focal Multi-scale Shape-feature Distillation Network for Small Fasteners Detection in Electric Power Scene

In the electric power scene, fasteners play a pivotal role in securing and connecting electrical equipment, with small fastener detection (SFD) being crucial for ensuring operational stability. Despite the replacement of manual inspection methods by non-destructive techniques employing deep learning...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology 2024-10, p.1-1
Hauptverfasser: Yi, Junfei, Mao, Jianxu, Zhang, Hui, Li, Mingjie, Zeng, Kai, Feng, Mingtao, Chang, Xiaojun, Wang, Yaonan
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container_title IEEE transactions on circuits and systems for video technology
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creator Yi, Junfei
Mao, Jianxu
Zhang, Hui
Li, Mingjie
Zeng, Kai
Feng, Mingtao
Chang, Xiaojun
Wang, Yaonan
description In the electric power scene, fasteners play a pivotal role in securing and connecting electrical equipment, with small fastener detection (SFD) being crucial for ensuring operational stability. Despite the replacement of manual inspection methods by non-destructive techniques employing deep learning, these approaches often demand substantial computational resources and involve numerous parameters. While knowledge distillation (KD) can be a viable solution, existing KD methods may often fail to achieve satisfactory performance when dealing with small object presentation and little inter-class variability in SFD tasks. To alleviate this, we propose a Focal Multi-scale Shape-feature Distillation Network (FMSD) to achieve efficient and precise fastener detection in electric power scenarios. Specifically, we propose a novel Multi-Scale Shape-Aware Feature Aggregation module (MSFA) to augment the network's perception of object shape and scale during the KD process. Additionally, we propose a Contour-Guided Distillation (CGD) module to optimize the transfer of the extracted shape-sensitive knowledge between the teacher and student models. Through a series of experiments compared with existing state-of-the-art (SOTA) methods, our method demonstrates superior performance over existing SOTA techniques, both efficiently and effectively. Furthermore, validation on publicly available power scene datasets confirms the generalizability and adaptability of our proposed FMSD across various settings.
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subjects contour-guided distillation
Detectors
Fasteners
Feature extraction
Inspection
knowledge distillation
Knowledge engineering
Object detection
Pins
Power systems
Proposals
shape-sensitive knowledge
Small fastener detection
Training
title FMSD: Focal Multi-scale Shape-feature Distillation Network for Small Fasteners Detection in Electric Power Scene
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