Lane Detection Based on ECBAM_ASPP Model
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8098 |
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| author | Xiang Gu Qiwei Huang Chaonan Du |
| author_facet | Xiang Gu Qiwei Huang Chaonan Du |
| author_sort | Xiang Gu |
| collection | DOAJ |
| description | With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality. |
| format | Article |
| id | doaj-art-39e505faa34a434ab0edd4d5e06b3f5d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-39e505faa34a434ab0edd4d5e06b3f5d2024-12-27T14:52:58ZengMDPI AGSensors1424-82202024-12-012424809810.3390/s24248098Lane Detection Based on ECBAM_ASPP ModelXiang Gu0Qiwei Huang1Chaonan Du2School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaSchool of Information Science and Technology, Nantong University, Nantong 226019, ChinaSchool of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaWith the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality.https://www.mdpi.com/1424-8220/24/24/8098lane detectionautonomous drivingattention mechanismdeep learning |
| spellingShingle | Xiang Gu Qiwei Huang Chaonan Du Lane Detection Based on ECBAM_ASPP Model Sensors lane detection autonomous driving attention mechanism deep learning |
| title | Lane Detection Based on ECBAM_ASPP Model |
| title_full | Lane Detection Based on ECBAM_ASPP Model |
| title_fullStr | Lane Detection Based on ECBAM_ASPP Model |
| title_full_unstemmed | Lane Detection Based on ECBAM_ASPP Model |
| title_short | Lane Detection Based on ECBAM_ASPP Model |
| title_sort | lane detection based on ecbam aspp model |
| topic | lane detection autonomous driving attention mechanism deep learning |
| url | https://www.mdpi.com/1424-8220/24/24/8098 |
| work_keys_str_mv | AT xianggu lanedetectionbasedonecbamasppmodel AT qiweihuang lanedetectionbasedonecbamasppmodel AT chaonandu lanedetectionbasedonecbamasppmodel |