RRepoGEO

REPOGEO REPORT · LITE

lucastabelini/LaneATT

Default branch main · commit 2f8583ba · scanned 6/7/2026, 11:32:26 PM

GitHub: 693 stars · 176 forks

AI VISIBILITY SCORE
84 /100
Healthy
Category recall
2 / 2
Avg rank #1.5 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface lucastabelini/LaneATT, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition core differentiator to the README's opening sentence

    Why:

    CURRENT
    This repository holds the source code for LaneATT, a novel state-of-the-art lane detection model proposed in the paper "_Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection_", by Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. De Souza, and Thiago Oliveira-Santos.
    COPY-PASTE FIX
    LaneATT is a novel state-of-the-art lane detection model that leverages a **feature attention mechanism** for enhanced representation and a **straight-line RANSAC algorithm** for robust line fitting. This repository holds the source code for LaneATT, as proposed in the paper "_Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection_" (CVPR 2021).
  • mediumtopics#2
    Add more specific topics related to application and methodology

    Why:

    CURRENT
    computer-vision, deep-learning, lane-detection, pytorch
    COPY-PASTE FIX
    computer-vision, deep-learning, lane-detection, pytorch, autonomous-driving, attention-mechanisms
  • lowreadme#3
    Add a dedicated 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ### Key Features

*   **Attention-guided Lane Detection:** LaneATT utilizes a novel feature attention mechanism to enhance feature representation, significantly improving accuracy and robustness in diverse driving conditions.
*   **Real-time Performance:** Designed for efficient, real-time operation, making it suitable for integration into autonomous driving systems.
*   **Robust Line Fitting:** Incorporates a straight-line RANSAC algorithm to reliably fit straight lines to detected lane points, even in challenging scenarios.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface lucastabelini/LaneATT
Avg rank
#1.5
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
YOLOP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLOP · recommended 2×
  2. U-Net · recommended 2×
  3. PINet · recommended 1×
  4. ResNet · recommended 1×
  5. FPN · recommended 1×
  • CATEGORY QUERY
    What are the best real-time deep learning models for accurate lane detection in autonomous driving?
    you: #2
    AI recommended (in order):
    1. YOLOP
    2. LaneATT ← you
    3. PINet
    4. ResNet
    5. FPN
    6. DeepLabv3+
    7. ENet
    8. U-Net
    Show full AI answer
  • CATEGORY QUERY
    How to implement robust lane detection with attention mechanisms using PyTorch for computer vision?
    you: #1
    AI recommended (in order):
    1. LaneATT ← you
    2. STC-Lane
    3. U-Net
    4. DeepLabV3+
    5. EfficientNet
    6. YOLOP
    7. DETR
    8. Swin Transformer
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of lucastabelini/LaneATT?
    pass
    AI did not name lucastabelini/LaneATT — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts lucastabelini/LaneATT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lucastabelini/LaneATT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo lucastabelini/LaneATT solve, and who is the primary audience?
    pass
    AI named lucastabelini/LaneATT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of lucastabelini/LaneATT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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lucastabelini/LaneATT — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite