Attention-based multi-scale feature fusion for free-space detection

Free space detection is a very important task in road scene understanding. With the continued development of convolutional neural networks, free-space detection can be seen as a class-specific semantic segmentation problem. In this paper, a new encoding-decoding network structure-HRUnet is designed, which always maintains the input of high-resolution images in both the encoding and decoding phases. It extracts multi-scale information from RGB images and continuously fuses them, and finally achieves accurate spatial detection. In addition, in order to improve the accuracy of detection, the attention mechanism module-spin attention is proposed to achieve the interaction between channel and spatial dimensions when calculating channel attention, establish the come relationship between channel and space, reduce the loss of feature information, and further improve the accuracy of spatial detection. Experimental results show that the proposed neural network structure outperforms current popular models in terms of balanced the computational complexity and accuracy.