WaveMamba-YOLO: Combining frequency awareness and state-space modeling for defect localization
Aping Ge, Yang Lv, Jun Huang
TL;DR
WaveMamba-YOLO is a new framework for detecting steel surface defects that improves accuracy and efficiency using frequency-aware and state-space modeling techniques.
Contribution
The novel integration of frequency-domain enhancement and state-space modeling in a real-time defect detection framework.
Findings
WaveMamba-YOLO achieves 51.70% [email protected] and 58.60% precision on the Severstal Steel Defect dataset.
The model reaches 77.70% [email protected] on the NEU-DET dataset, outperforming existing lightweight detectors.
The proposed modules effectively balance detection accuracy and computational efficiency for industrial applications.
Abstract
Steel surface defect detection is critical for ensuring the reliability and safety of automotive manufacturing. However, existing methods often suffer from high computational cost, weak sensitivity to fine textures, and limited adaptability to diverse defect scales. To address these challenges, we propose WaveMamba-YOLO, a real-time detection framework that integrates frequency-domain enhancement with efficient state-space modeling. The architecture introduces three key modules: (1) CHDWT, which combines Haar wavelet decomposition and residual learning to preserve structural details during downsampling; (2) GLaM, a global-local-aware Mamba module that couples large-kernel convolution with state-space modeling to capture long-range dependencies at linear complexity; and (3) LWGA, a lightweight group attention mechanism that adaptively attends to micro-, regular-, medium-, and large-scale…
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Taxonomy
TopicsAdvanced Neural Network Applications · Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring