UAV Obstacle Avoidance Model Based on Multi-axis Self-attention
To address the proneness of UAV crash due to rotor collision during flight,an improved image recognition model is proposed to achieve automatic warning.A bottleneck multi-axis self-attention module(BMSA)is embedded into the image recognition model for improvement,enabling the model to improve the recognition accuracy of the model for fine objects.The multi-axis self-attentive layer replaces the original convolutional layer in the low-resolution stage,enabling the model to obtain both local self-attention and global self-attention.The experiments show that the improved multi-axis self-attentive residual network(MS-ResNet)has high accuracy of obstacle recognition and achieve a better early warning effect.
国家科技期刊平台
NSTL
万方数据
