Lane Detection Based on ECBAM_ASPP Model
Abstract
1. Introduction
- In this paper, we propose the Efficient Convolutional Block Attention Module (ECBAM), which comprises two submodules: the Efficient Channel Attention Module (ECAM) and the Spatial Attention Module (SAM). ECBAM enhances the traditional Convolutional Block Attention Module (CBAM) by employing a dynamic one-dimensional convolution kernel in place of a shared Multi-Layer Perceptron (MLP).
- Building on the ECBAM and ASPP modules, this paper introduces the ECBAM_ASPP model. The ASPP module extracts multi-scale input features by applying different sampling rates, generating richer feature maps. These feature maps are combined with the attention weights generated by the ECBAM module to focus on lane areas while suppressing background interference.
- We evaluated the proposed ECBAM_ASPP model on the TuSimple and CULane datasets. The experimental results demonstrate that the ECBAM_ASPP model significantly enhances frames per second (FPS) while maintaining high accuracy. This indicates that the model ensures reliable lane line recognition while achieving high FPS, enabling vehicles to perceive road conditions in real time and make timely and accurate decisions.
2. Related Work
2.1. Lane Detection Methods Based on Conventional Image Processing
2.2. Lane Detection Methods Based on Deep Learning
2.3. Attention Mechanism
3. Methodology
3.1. Atrous Spatial Pyramid Pooling (ASPP)
3.2. Efficient Channel Attention (ECA)
3.3. Efficient Convolution Block Attention Module (ECBAM)
3.3.1. Convolutional Block Attention Module (CBAM)
3.3.2. Efficient Convolution Block Attention Module (ECBAM)
3.4. ECBAM_ASPP Model
3.5. Lane Detection
4. Experiments
4.1. Datasets
4.2. Experimental Environment and Details
4.3. Evaluation Metrics
4.4. Comparative Analysis of Experiments
4.4.1. Tusimple Dataset
4.4.2. CULane Dataset
4.5. Ablation Experiments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ECBAM | Efficient Convolution Block Attention Module |
CBAM | Convolutional Block Attention Module |
ASPP | Atrous Spatial Pyramid Pooling |
ECBAM_ASPP | Efficient Convolution Block Attention Module_Atrous Spatial Pyramid Pooling |
ECA | Efficient Channel Attention |
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Dataset | Frame | Train | Validation | Test | Lane | Resolution | Scenarios |
---|---|---|---|---|---|---|---|
Tusimple | 6408 | 3268 | 358 | 2782 | 1 | ||
CULane | 133,235 | 88,880 | 9675 | 34,680 | 9 |
Method Name | Accuracy (%) | F1 (%) | FP (%) | FN (%) | FPS (f/s) |
---|---|---|---|---|---|
PolyLaneNet | 93.36 | 90.62 | 9.42 | 9.33 | 115 |
FastDraw | 95.2 | 93.92 | 7.6 | 4.5 | 90 |
PINet | 95.81 | 95.39 | 5.85 | 3.36 | 40 |
EL-GAN | 94.9 | 96.26 | 4.12 | 3.36 | 10 |
SAD | 96.64 | 95.92 | 6.02 | 2.05 | 75 |
SCNN | 96.53 | 95.97 | 6.17 | 1.8 | 7.5 |
UFLD | 95.86 | 88.02 | 18.91 | 3.75 | 169.5 |
RATS | 96.16 | - | 18.3 | 3.62 | 118 |
MKD | 96.27 | - | 1.79 | 3.8 | 214.6 |
ECBAM_ASPP | 95.78 | 87.67 | 19.7 | 4.7 | 219 |
Method Name | Total | Normal | Crowded | Dazzle | Shawdow | No line | Arrow | Curve | Cross | Night | FPS |
---|---|---|---|---|---|---|---|---|---|---|---|
SCNN | 71.6 | 90.6 | 69.7 | 58.5 | 66.9 | 43.4 | 84.1 | 64.4 | 1990 | 66.1 | 7.5 |
SAD | 70.7 | 89.9 | 68.5 | 59.9 | 67.7 | 42.2 | 83.8 | 66.0 | 1960 | 64.6 | 19.8 |
UFLD | 72.3 | 90.7 | 70.2 | 59.5 | 69.3 | 44.4 | 85.7 | 69.5 | 2037 | 66.7 | 175.4 |
LaneATT | 76.68 | 92.14 | 75.03 | 66.47 | 78.15 | 49.39 | 88.38 | 67.72 | 1330 | 70.72 | 129 |
CondLaneNet | 78.74 | 93.38 | 77.14 | 71.17 | 79.93 | 51.85 | 89.89 | 73.88 | 1387 | 73.92 | 128 |
GANet | 79.39 | 93.73 | 77.92 | 71.64 | 79.49 | 52.63 | 90.37 | 76.32 | 1368 | 73.67 | 127 |
O2SFormer | 77.03 | 92.5 | 75.25 | 70.93 | 77.72 | 50.97 | 87.63 | 68.1 | 2749 | 72.88 | - |
MKD | 79.69 | 93.62 | 78.93 | 61.41 | 80.56 | 61.81 | 88.58 | 76.56 | 3102 | 76.36 | 214.8 |
ECBAM_ASPP | 78.45 | 94.08 | 77.17 | 68.56 | 79.20 | 51.18 | 88.72 | 75.41 | 2559 | 73.71 | 227.3 |
Group Number | ASPP Module | ECBAM Module | Accuracy (%) |
---|---|---|---|
1 | 95.06 | ||
2 | ✓ | 95.35 | |
3 | ✓ | 95.48 | |
4 | ✓ | ✓ | 95.78 |
Group Number | CBAM Module | ECBAM Module | Accuracy (%) |
---|---|---|---|
1 | 95.35 | ||
2 | ✓ | 95.42 | |
3 | ✓ | 95.78 |
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Gu, X.; Huang, Q.; Du, C. Lane Detection Based on ECBAM_ASPP Model. Sensors 2024, 24, 8098. https://doi.org/10.3390/s24248098
Gu X, Huang Q, Du C. Lane Detection Based on ECBAM_ASPP Model. Sensors. 2024; 24(24):8098. https://doi.org/10.3390/s24248098
Chicago/Turabian StyleGu, Xiang, Qiwei Huang, and Chaonan Du. 2024. "Lane Detection Based on ECBAM_ASPP Model" Sensors 24, no. 24: 8098. https://doi.org/10.3390/s24248098
APA StyleGu, X., Huang, Q., & Du, C. (2024). Lane Detection Based on ECBAM_ASPP Model. Sensors, 24(24), 8098. https://doi.org/10.3390/s24248098