一种基于语义分割的机场跑道检测算法

针对复杂电磁作战环境下无人机自主着陆应用场景,提出了一种基于图像语义分割的机场跑道检测算法,并设计构建了轻量高效的端到端跑道检测神经网络RunwayNet。在特征提取部分,使用空洞卷积对ShuffleNet V2进行改造,得到了输出特征图分辨率可调的主干网络,并利用自注意力机制设计了自注意力网络模块,使网络具备全局跑道特征提取能力。设计了解码器模块将网络浅层丰富的细节、空间位置信息与顶层粗略、抽象的语义分割信息相融合,从而获得精细的跑道检测输出结果。实验结果表明,RunwayNet网络在无人机着陆全过程都可以对跑道区域进行精准的分割识别,并且在嵌入式计算平台上能达到接近实时的处理速度,具有很强的实用价值。

An Airport Runway Detection Algorithm Based on Semantic Segmentation

For the application scenarios of autonomous landing of UAVs in complex electromag-netic combat environments, an airport runway detection algorithm based on image semantic segmentation is proposed, and a lightweight and efficient end-to-end runway detection neural network called RunwayNet is designed and constructed. In the feature extraction part, ShuffleNet V2 is modified by using atrous convolution to obtain a backbone network with adjustable output feature map resolution. Self-attention module is designed using the self-attention mechanism to make the network capable of global runway feature extraction. And the decoder module is designed to fuse the rich details, the spatial location information of the low-level layers, and the rough, abstract semantic segmentation information of the high-level layers to obtain a fine runway detection output. The experimental results show that RunwayNet can accurately segment the runway area during the entire landing of the UAVs, and can achieve near real-time processing speed on the embedded computing platform, which has strong practical value.