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TuSimple Benchmark (Lane Detection)
Lane Detection is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.
Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.
The archive carries no text for this table; the description above is the archive's text for the task Lane Detection. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: Accuracy (higher is better), F1 score (higher is better). Points are placed at the row's paper date; 40 of 43 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | SCNN_UNet_Attention_PL* | 98.38 | ✓ | Paper | – | 2023 | no code linked | report | |
| 2 | CLRNetV2 (ResNet18) | 96.99 | 97.90 | – | Paper | – | 2025 | no code linked | report |
| 3 | PE-RESA | 96.93 | – | Paper | – | 2022 | no code linked | report | |
| 4 | FOLOLane(ERFNet) | 96.92 | – | Paper | – | 2021 | no code linked | report | |
| 5 | CLRNet(ResNet-34) | 96.9% | 97.82 | – | Paper | Code | 2022 | 0 of 4 ran · 4 unverified | report |
| 6 | CLRNetV2 (ResNet34) | 96.88 | 97.95 | – | Paper | – | 2025 | no code linked | report |
| 7 | CLRNet(ResNet-18) | 96.82% | 97.89 | – | Paper | Code | 2022 | 0 of 4 ran · 4 unverified | report |
| 8 | RESA | 96.82 | 96.93 | – | Paper | Code | 2020 | linked, not harvested | report |
| 9 | CLLD | 96.82 | – | Paper | Code | 2023 | linked, not harvested | report | |
| 10 | CANet-L(ResNet101) | 96.76% | 97.77 | – | Paper | – | 2023 | no code linked | report |
| 11 | CANet-M | 96.66% | 97.44 | – | Paper | – | 2023 | no code linked | report |
| 12 | ENet-SAD | 96.64% | 95.92 | – | Paper | Code | 2019 | 7 of 8 ran · 1 unverified | report |
| 13 | HarD-SP | 96.58% | 96.38 | – | Paper | Code | 2020 | linked, not harvested | report |
| 14 | CANet-S | 96.56% | 97.51 | – | Paper | – | 2023 | no code linked | report |
| 15 | CondLaneNet-L(ResNet-101) | 96.54% | 97.24 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report |
| 16 | Oblique Convolution | 96.50% | 97.42 | – | – | – | not matched | report | |
| 17 | Pairwise pixel supervision + FCN | 96.50% | 94.31 | – | Paper | Code | 2018 | linked, not harvested | report |
| 18 | EL-GAN | 96.40% | 96.26 | – | Paper | – | 2018 | no code linked | report |
| 19 | LaneNet | 96.4% | 94.80 | – | Paper | Code | 2018 | 3 of 34 ran · 31 unverified | report |
| 20 | Discriminative loss function | 96.40% | – | Paper | Code | 2017 | 0 of 2 ran · 2 unverified | report | |
| 21 | ENet-Label | 96.29% | 95.23 | – | Paper | – | 2019 | no code linked | report |
| 22 | R-34-E2E | 96.22% | 96.58 | – | Paper | Code | 2020 | linked, not harvested | report |
| 23 | LSTR | 96.18 | 96.68 | – | Paper | Code | 2020 | linked, not harvested | report |
| 24 | R-50-E2E | 96.11% | 96.37 | – | Paper | Code | 2020 | linked, not harvested | report |
| 25 | LaneATT (ResNet-122) | 96.10% | 96.06 | – | Paper | Code | 2020 | 1 of 4 ran · 3 unverified | report |
| 26 | ERF-E2E | 96.02% | 96.25 | – | Paper | Code | 2020 | linked, not harvested | report |
| 27 | Lane-LSQ | 95.84% | – | – | – | not matched | report | ||
| 28 | BézierLaneNet (ResNet-34) | 95.65% | – | Paper | Code | 2022 | 12 of 12 ran · 0 unverified | report | |
| 29 | LaneAF | 95.64% | 96.49 | – | Paper | Code | 2021 | linked, not harvested | report |
| 30 | LaneATT (ResNet-34) | 95.63% | 96.77 | – | Paper | Code | 2020 | 1 of 4 ran · 3 unverified | report |
| 31 | Eigenlanes (ResNet-18) | 95.62% | – | Paper | Code | 2022 | 10 of 15 ran · 5 unverified | report | |
| 32 | LaneATT (ResNet-18) | 95.57% | 96.71 | – | Paper | Code | 2020 | 1 of 4 ran · 3 unverified | report |
| 33 | CondLaneNet(ResNet-18) | 95.48% | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report | |
| 34 | BézierLaneNet (ResNet-18) | 95.41% | – | Paper | Code | 2022 | 12 of 12 ran · 0 unverified | report | |
| 35 | CondLaneNet-M(ResNet-34) | 95.37% | 96.98 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report |
| 36 | End-to-end ERFNet | 95.24% | 90.82 | – | Paper | Code | 2019 | linked, not harvested | report |
| 37 | ERFNet | 94.5% | – | – | – | not matched | report | ||
| 38 | PolyLaneNet | 93.36% | 90.62 | – | Paper | Code | 2020 | 6 of 10 ran · 4 unverified | report |
| 39 | GANet(ResNet-34) | 97.71 | – | Paper | Code | 2022 | 6 of 7 ran · 1 unverified | report | |
| 40 | GANet(ResNet-18) | 97.68 | – | Paper | Code | 2022 | 6 of 7 ran · 1 unverified | report | |
| 41 | CLRNet(ResNet-101) | 97.62 | – | Paper | Code | 2022 | 0 of 4 ran · 4 unverified | report | |
| 42 | GANet(ResNet-101) | 97.45 | – | Paper | Code | 2022 | 6 of 7 ran · 1 unverified | report | |
| 43 | CondLaneNet(ResNet-34) | 97.01 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report |
All 43 rows shown. 40 link to a paper page on this site; 1 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 20 rows have a graph line, from 10 distinct papers; 16 rows (8 papers) have at least one sample that ran. Counting each paper once: Syntology ran 46 of 99 samples; 53 unverified. Separately, 3 of those 99 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-25. Per-sample status is on the paper page.
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