SMR-Net:Robot Snap Detection Based on Multi-Scale Features and Self-Attention Network

• URL: http://arxiv.org/abs/2603.01036v1
• Date: Sun, 01 Mar 2026 10:28:01 GMT
• Title: SMR-Net:Robot Snap Detection Based on Multi-Scale Features and Self-Attention Network
• Authors: Kuanxu Hou,
• Abstract summary: Traditional visual methods suffer from poor robustness and large localization errors when handling complex scenarios.<n>This paper proposes SMR-Net, a self-attention-based multi-scale object detection algorithm.<n> Experimental results on Type A and Type B snap datasets show SMR-Net outperforms traditional Faster R-CNN significantly.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In robot automated assembly, snap assembly precision and efficiency directly determine overall production quality. As a core prerequisite, snap detection and localization critically affect subsequent assembly success. Traditional visual methods suffer from poor robustness and large localization errors when handling complex scenarios (e.g., transparent or low-contrast snaps), failing to meet high-precision assembly demands. To address this, this paper designs a dedicated sensor and proposes SMR-Net, an self-attention-based multi-scale object detection algorithm, to synergistically enhance detection and localization performance. SMR-Net adopts an attention-enhanced multi-scale feature fusion architecture: raw sensor data is encoded via an attention-embedded feature extractor to strengthen key snap features and suppress noise; three multi-scale feature maps are processed in parallel with standard and dilated convolution for dimension unification while preserving resolution; an adaptive reweighting network dynamically assigns weights to fused features, generating fine representations integrating details and global semantics. Experimental results on Type A and Type B snap datasets show SMR-Net outperforms traditional Faster R-CNN significantly: Intersection over Union (IoU) improves by 6.52% and 5.8%, and mean Average Precision (mAP) increases by 2.8% and 1.5% respectively. This fully demonstrates the method's superiority in complex snap detection and localization tasks.

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