Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning
It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/14/4657 |
| _version_ | 1797526053509398528 |
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| author | Yulin He Wei Chen Chen Li Xin Luo Libo Huang |
| author_facet | Yulin He Wei Chen Chen Li Xin Luo Libo Huang |
| author_sort | Yulin He |
| collection | DOAJ |
| description | It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the running speed. Therefore, we design a more efficient way to extract the features of lanes, including two phases: (1) Local feature extraction, which sets a series of predefined anchor lines, and extracts the local features through their locations. (2) Global feature aggregation, which treats local features as the nodes of the graph, and builds a fully connected graph by adaptively learning the distance between nodes, the global feature can be aggregated through weighted summing finally. Another problem that limits the performance is the information loss in feature compression, mainly due to the huge dimensional gap, e.g., from 512 to 8. To handle this issue, we propose a feature compression module based on decoupling representation learning. This module can effectively learn the statistical information and spatial relationships between features. After that, redundancy is greatly reduced and more critical information is retained. Extensional experimental results show that our proposed method is both fast and accurate. On the Tusimple and CULane benchmarks, with a running speed of 248 FPS, F1 values of 96.81% and 75.49% were achieved, respectively. |
| first_indexed | 2024-03-10T09:24:40Z |
| format | Article |
| id | doaj.art-5dba686b39ad409ba4fb05b54882097b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:24:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5dba686b39ad409ba4fb05b54882097b2023-11-22T04:54:16ZengMDPI AGSensors1424-82202021-07-012114465710.3390/s21144657Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation LearningYulin He0Wei Chen1Chen Li2Xin Luo3Libo Huang4College of Computer, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaIt is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the running speed. Therefore, we design a more efficient way to extract the features of lanes, including two phases: (1) Local feature extraction, which sets a series of predefined anchor lines, and extracts the local features through their locations. (2) Global feature aggregation, which treats local features as the nodes of the graph, and builds a fully connected graph by adaptively learning the distance between nodes, the global feature can be aggregated through weighted summing finally. Another problem that limits the performance is the information loss in feature compression, mainly due to the huge dimensional gap, e.g., from 512 to 8. To handle this issue, we propose a feature compression module based on decoupling representation learning. This module can effectively learn the statistical information and spatial relationships between features. After that, redundancy is greatly reduced and more critical information is retained. Extensional experimental results show that our proposed method is both fast and accurate. On the Tusimple and CULane benchmarks, with a running speed of 248 FPS, F1 values of 96.81% and 75.49% were achieved, respectively.https://www.mdpi.com/1424-8220/21/14/4657lane detectiongraph structurefeature compressiondisentangled representation learning |
| spellingShingle | Yulin He Wei Chen Chen Li Xin Luo Libo Huang Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning Sensors lane detection graph structure feature compression disentangled representation learning |
| title | Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning |
| title_full | Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning |
| title_fullStr | Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning |
| title_full_unstemmed | Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning |
| title_short | Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning |
| title_sort | fast and accurate lane detection via graph structure and disentangled representation learning |
| topic | lane detection graph structure feature compression disentangled representation learning |
| url | https://www.mdpi.com/1424-8220/21/14/4657 |
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