How Dynamic Snake Convolution Boosts Tubular Segmentation and Infrared Small Target Detection

Dynamic Snake Convolution (DSConv) for Tubular Structure Segmentation

DSConv iteratively offsets convolution kernels so that the receptive field follows elongated, curved structures, avoiding the drift problem of conventional deformable convolutions.

Key components

Dynamic Snake Convolution (DSConv) : adaptive kernel offsets that concentrate on tubular morphology.

Multi‑view Feature Fusion : generates several morphological kernel templates and applies random dropout to suppress redundant noise.

Topological Continuity Loss (TCLoss) : uses Persistent Homology to enforce topological continuity, reducing segmentation breaks.

Experimental results

Volume accuracy : Dice improves by 1.3%–3.4% and clDice by 0.8%–3.8% on DRIVE, Massachusetts Roads, and Cardiac CCTA datasets.

Topological continuity : β0/β1 errors drop by 0.2–0.3; Over‑First‑Error (OF) rises by 3.3%–6.0%.

Visualization : DSConv repairs vessel breaks missed by standard methods and focuses more precisely on tubular regions.

Paper link: https://arxiv.org/pdf/2307.08388

Pinwheel‑shaped Convolution (PConv) and Scale‑based Dynamic Loss (SD Loss) for Infrared Small Target Detection

PConv encodes the Gaussian spatial distribution of infrared small‑target (IRST) pixels into asymmetric kernels, expanding the receptive field by 177% with only a 22.2% increase in parameters. SD Loss adjusts IoU and position loss weights according to target area, mitigating IoU fluctuation for tiny objects.

Key components

Pinwheel‑shaped Convolution (PConv) : non‑symmetric kernels derived from IRST statistics, providing multi‑directional sparse connections.

Scale‑dynamic Loss (SD Loss) : dynamic coefficients β_B/β_M scale loss terms based on target size (δ parameter).

SIRST‑UAVB benchmark : UAV‑collected dataset of 3,000 images, smallest targets 9 px, with complex backgrounds.

Experimental results

Detection : YOLOv8n‑p2 + PConv(4,3) + SD Loss (δ=0.5) achieves 93.8% mAP₅₀ on SIRST‑UAVB, surpassing MixConv and AKConv.

Segmentation : MSHNet + PConv + SD Loss improves IoU from 66.82% to 68.49%; small‑target (<16 px) accuracy gains are significant.

Ablation : optimal PConv blade lengths (4, 3) and δ = 0.5 confirm synergistic effect.

Paper link: https://arxiv.org/abs/2412.16986

Overall insight

Injecting geometric or statistical priors into convolution kernels and coupling them with adaptive loss functions (topological constraints or scale‑dynamic weighting) substantially improves segmentation of thin structures and detection of tiny infrared targets.

Code example

来源：人工智能前沿讲习
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