Abstract:

Aiming at the problem of lane line detection accuracy of intelligent vehicles in complex scenes, this paper proposes a PANet lane line detection algorithm that incorporates a multi-scale spatial attention mechanism. The algorithm references the pre-anchored frame UFLD lane line detection model and combines the feature pyramid enhancement module PANet with depth-separable convolution to realize multi-scale feature extraction from images. In addition, a multi-scale spatial attention module is designed in the network framework and a SimAM lightweight attention mechanism is introduced to enhance the focusing ability on target features. After that, an adaptive feature fusion module is designed to perform cross-scale fusion of feature maps output from PANet by intelligently adjusting the fusion weights of feature maps at different scales, so as to effectively enhance the network's ability to extract complex features. Finally, the application of Tusimple dataset detection proves that the detection accuracy of this paper's algorithm is 96.84%, which is 1.03% better than the original algorithm and superior to the traditional mainstream algorithm; and based on the CULane dataset's detection of the nine scenarios proves that the comprehensive F1 value of this paper's algorithm is 72.74% better than the traditional mainstream algorithm, which is 4.34% better than the original algorithm, especially under the bright light, shadows, and other extreme scenes. Especially in strong light, shadow and other extreme scenes, the detection performance improvement is larger, which fully demonstrates that the detection method in this paper has better detection ability in complex scenes. In addition, the real-time test shows that the model inference speed reaches 118FPS, which meets the real-time demand of intelligent vehicles.

Key words: lane detection, deep learning, multi-scale spatial attention module, adaptively spatial feature fusion