M-LSD-tiny — LiteRT (on-device line segment detection, fully-GPU)
M-LSD (NAVER, AAAI 2022) light-weight real-time line segment
detection, converted to LiteRT and running fully on the CompiledModel GPU (ML Drift) on Android.
Detects straight line segments — building edges, document borders, wireframes, room layout. The tiny
variant (MobileNetV2 backbone, 0.62M params) is 1.4 MB in fp16.
On-device (Pixel 8a, Tensor G3 — verified)
| nodes on GPU | 99 / 99 LITERT_CL (full residency) |
| inference | ~2 ms (512×512) |
| size | 1.4 MB (fp16) |
| accuracy | device-vs-PyTorch corr 0.997 (127 vs 128 lines decoded) |
image[1,4,512,512] (RGB + ones channel, scaled to [-1,1]) →[GPU: MobileNetV2 U-Net]→ tpMap[1,9,256,256]
The output is a "TP map": channel 0 = line-center heatmap, channels 1–4 = start/end displacement. The decode (sigmoid + 3×3 NMS over centers, displacement → endpoints, ×2) runs on the host.
How it converts (litert-torch)
Pure CNN encoder-decoder. A single re-authoring: the decoder's F.interpolate(bilinear, align_corners=True)
→ align_corners=False (the Mali delegate bans align_corners=True + half-pixel). MobileNetV2 has no
max-pool (strided convs → no PADV2), and the upsample is RESIZE_BILINEAR, not a transposed conv → fully
GPU-clean. Result: banned ops NONE, all tensors ≤4D, tflite-vs-torch corr 1.0, device-vs-torch corr 0.997.
Preprocessing & decode
Resize to 512×512, append a 4th channel of ones, scale (x/127.5) - 1, NCHW. Decode: sigmoid the center map,
3×3 max NMS, threshold (0.10), displacement → endpoints, filter by length, ×2 to 512-space.
License
Apache-2.0. Upstream: navervision/mlsd; PyTorch port lhwcv/mlsd_pytorch.
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