496 general conduct research into detection methods for lane recognition #502
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (3)
doc/research/paf24/perception/lane_detection_research.md (3)
13-16: Enhance formula explanation sectionThe reference to evaluation formulas lacks proper explanation. Consider adding:
- Brief description of each metric
- When/why each metric is important
- How these metrics compare to other evaluation methods
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[uncategorized] ~13-~13: Possible missing comma found.
Context: ...different lane detection models. In the following the best perfoming model of the three m...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~13-~13: It seems likely that a singular genitive (’s) apostrophe is missing.
Context: ...most common Datasets are presented. The models evaluation is based on the calculation ...(AI_HYDRA_LEO_APOSTROPHE_S_XS)
111-115: Standardize performance metrics in summary tableThe summary table has inconsistent metric reporting (some models missing Test_Acc). Consider:
- Using consistent metrics across all models
- Adding a note explaining why certain metrics are unavailable
- Adding hyperlinks to the corresponding sections
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🪛 Markdownlint
115-115: Expected: 0 or 2; Actual: 1
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75-75: Clean up document formattingSeveral formatting issues need attention:
- Remove multiple consecutive blank lines (lines 79-80, 98, 108)
- Break down long paragraphs (line 75, 119) into smaller, more readable chunks
- Ensure consistent spacing around lists
Also, consider adding a comma after "In the following" in line 13 and fix the missing apostrophe in "model's evaluation".
Also applies to: 79-80, 98-98, 108-108, 119-119
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doc/research/paf24/perception/lane_detection_research.md(1 hunks)
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doc/research/paf24/perception/lane_detection_research.md
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🔇 Additional comments (1)
doc/research/paf24/perception/lane_detection_research.md (1)
1-128: Overall assessment: Comprehensive research with minor improvements needed
The document provides valuable insights into lane detection models and their performance across different datasets. The structure is logical, and the content is informative. The suggested improvements focus on:
- Consistency in navigation and formatting
- Robustness of image references
- Completeness of technical explanations
- Standardization of performance metrics
These changes will enhance the document's maintainability and usefulness as a reference.
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[uncategorized] ~13-~13: Possible missing comma found.
Context: ...different lane detection models. In the following the best perfoming model of the three m...
(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~13-~13: It seems likely that a singular genitive (’s) apostrophe is missing.
Context: ...most common Datasets are presented. The models evaluation is based on the calculation ...
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (2)
doc/research/paf24/perception/lane_detection_research.md (2)
13-14: Enhance the evaluation metrics explanationThe document mentions accuracy, precision, recall, and F1-measure but doesn't explain what these metrics mean in the context of lane detection. Consider adding brief descriptions of how each metric relates to lane detection performance.
Add explanations like:
- Accuracy: Percentage of correctly identified lane pixels
- Precision: Ratio of correctly identified lane pixels to total predicted lane pixels
- Recall: Ratio of correctly identified lane pixels to actual lane pixels
- F1-measure: Harmonic mean of precision and recall
96-107: Improve list formatting consistencyThe advantages and disadvantages lists have inconsistent formatting and spacing. Some items have extra blank lines while others don't.
Apply consistent formatting:
- Add a blank line before and after each list
- Remove extra blank lines between list items
- Use consistent capitalization and punctuation for list items
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doc/research/paf24/perception/lane_detection_research.md(1 hunks)
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doc/research/paf24/perception/lane_detection_research.md
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[grammar] ~123-~123: The past tense and past participle of the verb “to choose” is “chose”. The past participle is “chosen”.
Context: ....org/pdf/2304.13242v2) ## Decision We choosed to implement the CLRerNet-DLA34 and the...
(CHOOSED)
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| ## Decision | ||
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| We choosed to implement the CLRerNet-DLA34 and the CondLSTR(ResNet-101) because they have already implemented code bases to save time. | ||
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🛠️ Refactor suggestion
Enhance the decision section
The decision section would benefit from a more detailed explanation of why these specific models were chosen beyond code availability.
Consider adding:
- Performance comparison between the chosen models
- Trade-offs considered (accuracy vs. computational requirements)
- Implementation complexity assessment
- Specific advantages that make these models suitable for your use case
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🪛 LanguageTool
[grammar] ~123-~123: The past tense and past participle of the verb “to choose” is “chose”. The past participle is “chosen”.
Context: ....org/pdf/2304.13242v2) ## Decision We choosed to implement the CLRerNet-DLA34 and the...
(CHOOSED)
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
doc/research/paf24/perception/lane_detection_research.md (2)
79-80: Fix markdown formatting issuesThere are several formatting issues that affect readability:
- Remove multiple consecutive blank lines (lines 79-80, 98, 108, 125)
- Add blank lines around lists (before line 96 and 104)
Also applies to: 98-98, 108-108, 125-125
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111-115: Clarify missing metrics in comparison tableThe table shows "---" for Test_Acc in CLRerNet-DLA34 and CondLSTR entries. If these metrics are unavailable, consider:
- Adding a note explaining why these metrics are not available
- Using alternative metrics that are available for comparison
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🪛 Markdownlint (0.35.0)
115-115: Expected: 0 or 2; Actual: 1
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doc/research/paf24/perception/lane_detection_research.md(1 hunks)
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doc/research/paf24/perception/lane_detection_research.md
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🔇 Additional comments (1)
doc/research/paf24/perception/lane_detection_research.md (1)
117-117: Fix inconsistent image path
The image path /doc/assets/perception/Comparison_of_CurveLane_Dataset.jpg uses an absolute path that differs from other image references in the document.
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+| ## Decision | ||
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| We chose to implement the CLRerNet-DLA34 and the CondLSTR(ResNet-101) because they have already implemented code bases to save time. |
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🛠️ Refactor suggestion
Enhance the decision section with technical justification
The current decision rationale focuses solely on code availability. Consider adding:
- Technical comparison between CLRerNet-DLA34 and CondLSTR
- How these models align with your specific requirements
- Performance trade-offs considered
- Resource constraints and deployment considerations
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (3)
doc/research/paf24/perception/lane_detection_research.md (3)
58-59: Enhance quantitative comparisonWhile the advantages are well described, consider adding specific performance metrics to support the claim of "improved accuracy". For example, include F1 scores for different scenarios (curves, difficult lighting) to provide concrete evidence of improvement.
118-120: Add implementation details and requirementsSince these models have been selected for implementation, consider adding:
- Required dependencies and system requirements
- Expected setup and configuration steps
- Potential integration challenges
- Performance benchmarks on your specific use case
Would you like me to help create a template for documenting these implementation details?
111-111: Fix Markdown formatting issuesPlease address the following formatting issues:
- Remove trailing space in line 111
- Remove extra blank line at 122
- Add blank lines around lists at lines 94 and 101
Also applies to: 122-122
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doc/research/paf24/perception/lane_detection_research.md(1 hunks)
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doc/research/paf24/perception/lane_detection_research.md
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🔇 Additional comments (2)
doc/research/paf24/perception/lane_detection_research.md (2)
1-7: LGTM! Well-structured table of contents
The document structure is clear and the navigation links are properly formatted.
113-114: Fix image path format
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
doc/research/paf24/perception/lane_detection_research.md (2)
3-6: Enhance table of contents with subsectionsConsider expanding the table of contents to include subsections for better navigation:
- [Overall](#overall) - [Lane Detection on TuSimple](#lane-detection-on-tusimple) - [Lane Detection on CULane](#lane-detection-on-culane) - [Lane Detection on CurveLanes](#lane-detection-on-curvelanes) + [Overall](#overall) + [Lane Detection on TuSimple](#lane-detection-on-tusimple) + - [SCNN_UNet_Attention_PL Advantages](#scnn_unet_attention_pl-advantages) + - [SCNN_UNet_Attention_PL Disadvantages](#scnn_unet_attention_pl-disadvantages) + [Lane Detection on CULane](#lane-detection-on-culane) + - [CLRerNet-DLA34 Advantages](#clrernet-dla34-advantages) + - [CLRerNet-DLA34 Disadvantages](#clrernet-dla34-disadvantages) + [Lane Detection on CurveLanes](#lane-detection-on-curvelanes) + - [CondLSTR (ResNet-101)](#condlstr-resnet-101) + [Summary](#summary) + [Decision](#decision)
13-13: Fix grammar and improve readabilitySeveral grammar improvements needed:
- In the following the best perfoming model of the three most common Datasets are presented. + In the following, the best-performing model of the three most common datasets is presented. - The resolution of image is 1280×720. + The resolution of the image is 1280×720. - dataset is divided into 88880 images for training set, 9675 for validation set + dataset is divided into 88880 images for the training set, 9675 for the validation setAlso applies to: 20-20, 49-49
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[uncategorized] ~13-~13: Possible missing comma found.
Context: ...different lane detection models. In the following the best perfoming model of the three m...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~13-~13: It seems likely that a singular genitive (’s) apostrophe is missing.
Context: ...most common Datasets are presented. The models evaluation is based on the calculation ...(AI_HYDRA_LEO_APOSTROPHE_S_XS)
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doc/research/paf24/perception/lane_detection_research.md(1 hunks)
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doc/research/paf24/perception/lane_detection_research.md
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🔇 Additional comments (3)
doc/research/paf24/perception/lane_detection_research.md (3)
116-116: Fix incorrect image path
122-124: Enhance decision section with technical justification
110-114: Complete missing accuracy metrics in summary table
The summary table has missing accuracy values (marked as "---") for CLRerNet-DLA34 and CondLSTR. If these metrics are available in the referenced papers, please include them for a fair comparison.
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🧹 Outside diff range and nitpick comments (3)
doc/research/paf24/perception/lane_detection_research.md (3)
3-6: Enhance table of contents for better navigationConsider expanding the table of contents to include subsections and adding brief descriptions:
- - [Overall](#overall) - - [Lane Detection on TuSimple](#lane-detection-on-tusimple) - - [Lane Detection on CULane](#lane-detection-on-culane) - - [Lane Detection on CurveLanes](#lane-detection-on-curvelanes) + - [Overall](#overall) + - Introduction to lane detection + - Evaluation metrics + - [Lane Detection on TuSimple](#lane-detection-on-tusimple) + - SCNN_UNet_Attention_PL model analysis + - [Lane Detection on CULane](#lane-detection-on-culane) + - CLRerNet-DLA34 model analysis + - [Lane Detection on CurveLanes](#lane-detection-on-curvelanes) + - CondLSTR (ResNet-101) model analysis + - [Summary](#summary) + - [Decision](#decision)
112-116: Enhance summary table with additional metricsThe summary table could be more informative with additional columns:
- Model architecture/backbone
- Inference time/FPS
- Memory requirements
- Hardware requirements for training/inference
This would help readers better understand the practical implications of choosing each model.
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13-13: Fix grammar and improve readabilityApply these corrections:
- The website PapersWithCode [1] gives several benchmarks for different lane detection models. In the following the best perfoming model of the three most common Datasets are presented. + The website PapersWithCode [1] provides several benchmarks for different lane detection models. The following sections present the best-performing models from the three most common datasets. - CULane is a large scale challenging dataset for academic research on traffic lane detection. + CULane is a large-scale, challenging dataset for academic research on traffic lane detection. - The dataset is divided into 88880 images for training set, 9675 for validation set, and 34680 for test set. + The dataset is divided into 88,880 images for the training set, 9,675 for the validation set, and 34,680 for the test set.Also applies to: 50-51
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doc/research/paf24/perception/lane_detection_research.md
[uncategorized] ~50-~50: Possible missing comma found.
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🪛 Markdownlint (0.35.0)
doc/research/paf24/perception/lane_detection_research.md
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (2)
doc/research/paf24/perception/lane_detection_research.md (2)
3-6: Consider adding References to the table of contentsThe document includes references at the end, but they're not listed in the table of contents. Consider adding:
- [Overall](#overall) - [Lane Detection on TuSimple](#lane-detection-on-tusimple) - [Lane Detection on CULane](#lane-detection-on-culane) - [Lane Detection on CurveLanes](#lane-detection-on-curvelanes) +- [References](#references)
50-51: Enhance readability of CULane dataset descriptionConsider restructuring the dataset description for better readability:
-CULane is a large scale challenging dataset for academic research on traffic lane detection. It is collected by cameras mounted on six different vehicles driven by different drivers in Beijing. More than 55 hours of videos were collected and 133,235 frames were extracted. -The dataset is divided into 88880 images for training set, 9675 for validation set, and 34680 for test set. The test set is divided into normal and 8 challenging categories. [4] +CULane is a large-scale challenging dataset for academic research on traffic lane detection, consisting of: +- Collection method: Cameras mounted on six different vehicles driven by different drivers in Beijing +- Total data: Over 55 hours of videos, yielding 133,235 extracted frames +- Dataset split: + - Training set: 88,880 images + - Validation set: 9,675 images + - Test set: 34,680 images (divided into normal and 8 challenging categories) + +[4]🧰 Tools
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[uncategorized] ~50-~50: Possible missing comma found.
Context: ... Detection on CULane CULane is a large scale challenging dataset for academic resear...(AI_HYDRA_LEO_MISSING_COMMA)
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[uncategorized] ~50-~50: Possible missing comma found.
Context: ... Detection on CULane CULane is a large scale challenging dataset for academic resear...
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doc/research/paf24/perception/lane_detection_research.md
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doc/research/paf24/perception/lane_detection_research.md (1)
126-128: Previous review comment is still applicable
| |CondLSTR(ResNet-101)|---|0.9132|0.8580|0.8847|2023|CurveLanes| | ||
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Fix incorrect image path
The image path uses /doc/assets/ which should be relative to the repository root.
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Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (4)
doc/research/paf24/perception/lane_detection_research.md (4)
10-11: Enhance the introduction with specific technical challengesConsider expanding the introduction to include specific technical challenges in lane detection, such as:
- Varying road conditions (wet roads, faded markings)
- Environmental factors (shadows, glare, night conditions)
- Complex scenarios (intersections, merging lanes)
- Real-time processing requirements
22-34: Add quantitative performance metrics for pre-training stageThe description of the two-stage training process would benefit from specific metrics showing the improvement gained from the pre-training stage. Consider adding:
- Pre-training convergence metrics
- Performance comparison with and without pre-training
- Resource requirements and training time
64-66: Elaborate on the performance gap analysisThe mention of the performance gap (81.43% vs 98.47%) would be more insightful with:
- Analysis of factors contributing to this gap
- Potential approaches to bridge this gap
- Impact of this gap on real-world applications
107-109: Add real-world performance metricsThe computational complexity concern would be more actionable with specific metrics:
- Inference time on standard hardware
- Memory requirements
- Minimum hardware specifications for real-time operation
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[uncategorized] ~20-~20: You might be missing the article “the” here.
Context: ...mages on US highways. The resolution of image is 1280×720. The dataset is composed of...
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[uncategorized] ~51-~51: You might be missing the article “the” here.
Context: ...ataset is divided into 88880 images for training set, 9675 for validation set, and 34680...
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[uncategorized] ~51-~51: You might be missing the article “the” here.
Context: ...88880 images for training set, 9675 for validation set, and 34680 for test set. The test s...
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[uncategorized] ~51-~51: You might be missing the article “the” here.
Context: ... 9675 for validation set, and 34680 for test set. The test set is divided into norma...
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doc/research/paf24/perception/lane_detection_research.md
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| | Model | Test Acc (%) | Precision | Recall | F1-Measure | Release | Dataset | | ||
| |--------|-------------|-----------|---------|------------|---------|----------| | ||
| | SCNN_UNet_Attention_PL | 98.36 | 0.937 | 0.911 | 0.924 | 2022 | TuSimple | | ||
| | CLRerNet-DLA34 | N/A | 0.917 | 0.818 | 0.814 | 2023 | CULane | | ||
| | CondLSTR(ResNet-101) | N/A | 0.913 | 0.858 | 0.885 | 2023 | CurveLanes | |
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🛠️ Refactor suggestion
Explain missing test accuracy values
The summary table shows "N/A" for test accuracy of CLRerNet-DLA34 and CondLSTR. Please:
- Explain why these values are not available
- Provide alternative metrics if test accuracy cannot be measured
- Consider using different evaluation metrics that are available across all models
| It should be mentioned that the paper by Robin Karlsson et. al was also read, but it was not worth comparing it here as it was a selfsupervised learning model and was not trained on the above datasets but on a smaller dataset and used more sensors and cameras | ||
| (6 cameras, 5 radars and 1 lidar) than allowed in the Qualify. | ||
| But for interrested people you can find the paper [here](https://arxiv.org/pdf/2304.13242v2) |
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🛠️ Refactor suggestion
Improve clarity of excluded research
The explanation for excluding Robin Karlsson's paper needs restructuring:
-It should be mentioned that the paper by Robin Karlsson et. al was also read, but it was not worth comparing it here as it was a selfsupervised learning model and was not trained on the above datasets but on a smaller dataset and used more sensors and cameras
-(6 cameras, 5 radars and 1 lidar) than allowed in the Qualify.
+The self-supervised learning model proposed by Karlsson et al. was excluded from comparison because:
+1. It uses a different training approach (self-supervised learning)
+2. It was trained on a smaller, incomparable dataset
+3. Its hardware requirements (6 cameras, 5 radars, and 1 lidar) exceed the project constraints📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| It should be mentioned that the paper by Robin Karlsson et. al was also read, but it was not worth comparing it here as it was a selfsupervised learning model and was not trained on the above datasets but on a smaller dataset and used more sensors and cameras | |
| (6 cameras, 5 radars and 1 lidar) than allowed in the Qualify. | |
| But for interrested people you can find the paper [here](https://arxiv.org/pdf/2304.13242v2) | |
| The self-supervised learning model proposed by Karlsson et al. was excluded from comparison because: | |
| 1. It uses a different training approach (self-supervised learning) | |
| 2. It was trained on a smaller, incomparable dataset | |
| 3. Its hardware requirements (6 cameras, 5 radars, and 1 lidar) exceed the project constraints | |
| But for interrested people you can find the paper [here](https://arxiv.org/pdf/2304.13242v2) |
…methods-for-lane-recognition
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🧹 Outside diff range and nitpick comments (3)
doc/research/paf24/perception/lane_detection_research.md (3)
13-14: Improve grammar and clarity in the introductionThe text needs better punctuation and more precise wording:
-The website PapersWithCode [1] gives several benchmarks for different lane detection models. In the following the best perfoming model of the three most common Datasets are presented. The models evaluation is based on the calculation of accuracy, precision, Recall, and F1-measure. +The website PapersWithCode [1] provides benchmarks for various lane detection models. In the following section, the best-performing models from the three most common datasets are presented. The models' evaluation is based on accuracy, precision, recall, and F1-measure calculations.🧰 Tools
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[uncategorized] ~13-~13: Possible missing comma found.
Context: ...different lane detection models. In the following the best perfoming model of the three m...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~13-~13: It seems likely that a singular genitive (’s) apostrophe is missing.
Context: ...most common Datasets are presented. The models evaluation is based on the calculation ...(AI_HYDRA_LEO_APOSTROPHE_S_XS)
55-57: Enhance CLRerNet-DLA34 technical descriptionThe CLRerNet-DLA34 section would benefit from more technical details, similar to the comprehensive coverage provided for CondLSTR. Consider adding:
- A diagram or visual representation of the architecture
- Detailed explanation of the row-based lane representation approach
- Technical specifications of the DLA34 backbone
- Performance metrics under different conditions
128-128: Add implementation considerations sectionConsider adding a new section after the decision that discusses:
- Hardware requirements for each model
- Real-time performance expectations
- Integration challenges and solutions
- Required preprocessing steps
- Potential optimization strategies
This would help future implementers understand the practical aspects of deploying these models.
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[uncategorized] ~13-~13: Possible missing comma found.
Context: ...different lane detection models. In the following the best perfoming model of the three m...
(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~13-~13: It seems likely that a singular genitive (’s) apostrophe is missing.
Context: ...most common Datasets are presented. The models evaluation is based on the calculation ...
(AI_HYDRA_LEO_APOSTROPHE_S_XS)
Description
A .md file where all research results are summarized to support the decision which model should be implemented for the lane detection
Fixes #496
Type of change
add .md file (research)
Does this PR introduce a breaking change?
e.g. is old functionality not usable anymore
Most important changes
new .md file
Summary by CodeRabbit