基于深度学习的无人机自主降落标识检测方法

Aiming at improving the real-time performance and accuracy of UAV autonomous landing, a landing marker detection method based on deep learning is proposed. Firstly, the lightweight network MobileNetv2 is used as the backbone network for feature extraction. Secondly, drawing on the network structure of YOLOv4, depthwise separable convolution is introduced to reduce the number of parameters without affecting model performance. Then, a feature pyramid module based on skip connection structures is proposed. With this module, the feature maps output from the backbone can be stitched and the detail information and semantic information can be fused to obtain features with stronger characterization capability. Finally, the detection head is optimized by depthwise separable convolution to complete the target detection task. Experiments are conducted on the Pascal VOC dataset and the landing marker dataset. The results show that the improved detection algorithm effectively reduces the computational and parameter complexity of the model, improves the detection speed, and can meet the accuracy requirements of autonomous UAV landing.