带注意力机制的神经网络用于跑道线检测

为了提高无人机着陆的有效性，设计了一种带有注意力机制的神经网络算法（LineNet）实现无人机跑道线检测。根据无人机着陆场景仿真出样本数据，并利用标注工具对数据进行标注，使用Shuffle Conv模块缓解特征融合计算量的占用问题，并引入空洞空间金字塔池化注意力机制（ASPP-SA）,ASPP-SA模块中添加残差的跳跃结构获取更多的图像信息，对LineNet模型的检测结果做形态学处理并结合连通区域约束对跑道线特征点进行分类，对相同类别的特征点通过最小二乘法进行跑道线拟合。经仿真数据验证，设计的方法可以有效地检测和识别出正确的跑道线，其平均检测精度为94.36%，相较于LaneNet算法、SegNet算法分别提高了12.82和9.95个百分点，单帧检测时间17.2 ms，是CNN+Hough变化算法1.5倍左右，可以满足无人机着陆的响应时间需求，在无人机着陆的研究过程中有重要意义。

Neural Network with Attention Mechanism for Runway Line Detection

To improve the effectiveness of UAV landing, a neural network algorithm（LineNet） with attention mechanism is designed to realize the detection of UAV runway lines. Firstly, the sample data is simulated according to the landing scene of the drone, and the data is marked with the labeling tool, Secondly, the Shuffle Conv module is used to alleviate the problem of feature fusion computation, and the hole space pyramid pooling attention mechanism（ASPP-SA） is introduced, it adds residual skip structure in ASPP-SA module to obtain more image information. Then, the detection results of the LineNet model are morphologically processed, and the feature points of the runway line are classified according to the constraints of the connected region. Finally, the feature points of the same category are fitted by the least square method. Verified by simulation data, the designed method can effectively detect and identify the correct runway line and its average detection accuracy is 94.36%. Compared with the LaneNet algorithm and the SegNet algorithm, the detection accuracy is improved by 12.82 and 9.95 percentage points. The detection time of a single frame is 17.2 ms, which is about 1.5 times that of the CNN+Hough change algorithm, which can meet the response time requirements of UAV landing and is of great significance in the research process of UAV landing.