Deep Integration: A Multi-Label Architecture for Road Scene Recognition.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Autonomous Systems

Background:

• Deep convolutional neural networks have advanced autonomous driving perception tasks like object detection.
• Scene-centric understanding and identification in autonomous driving remain challenging due to data limitations and training complexities.

Purpose of the Study:

• To address the lack of large-scale, annotated road scene datasets.
• To develop an effective deep learning model for multi-class road scene recognition, handling data imbalance and complex conditions.

Main Methods:

• Introduction of DrivingScene, a large-scale dataset with over 110,000 annotated images of diverse traffic scenarios.
• Proposal of a multi-label neural network incorporating single- and multi-class modes with a multi-level cost function.
• Utilization of a deep data integration strategy to enhance classification of challenging samples.

Main Results:

• The proposed multi-label neural network demonstrated effectiveness in handling imbalanced categories.
• Experimental results on the DrivingScene and PASCAL VOC datasets validated the approach's performance.
• The method improved classification ability on hard samples, addressing key training challenges.

Conclusions:

• The DrivingScene dataset is a significant contribution to road scene understanding research.
• The proposed multi-label neural network offers an effective solution for multi-class traffic scene classification.
• The approach successfully tackles data imbalance and improves the robustness of autonomous driving perception systems.