College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China.
Sci Rep. 2022 Aug 25;12(1):14474. doi: 10.1038/s41598-022-18263-z.
Airport aircraft identification has essential application value in conflict early warning, anti-runway foreign body intrusion, remote command, etc. The scene video images have problems such as small aircraft targets and mutual occlusion due to the extended shooting distance. However, the detection model is generally complex in structure, and it is challenging to meet real-time detection in air traffic control. This paper proposes a real-time detection network of scene video aircraft-RPD (Realtime Planes Detection) to solve this problem. We construct the lightweight convolution backbone network RPDNet4 for feature extraction. We design a new core component CBL module(Conv (Convolution), BN (Batch Normalization), RELU (Rectified Linear Units)) to expand the range of receptive fields in the neural network. We design a lightweight channel adjustment module block by adding separable depth convolution to reduce the model's structural parameters. The loss function of GIou loss improves the convergence speed of network training. the paper designs the four-scale prediction module and the adjacent scale feature fusion technology to fuse the adjacent features of different abstract levels. Furthermore, a feature pyramid structure with low-level to high-level is constructed to improve the accuracy of airport aircraft's small target detection. The experimental results show that compared with YOLOv3, Faster-RCNN, and SSD models, the detection accuracy of the RPD model improved by 5.4%, 7.1%, and 23.6%; in terms of model parameters, the RPD model was reduced by 40.5%, 33.7%, and 80.2%; In terms of detection speed, YOLOv3 is 8.4 fps while RPD model reaches 13.6 fps which is 61.9% faster than YOLOv3.
机场飞机识别在冲突预警、反跑道异物入侵、远程指挥等方面具有重要的应用价值。由于拍摄距离的延长，场景视频图像中小飞机目标和相互遮挡等问题突出。但是，检测模型通常结构复杂，在空管中难以满足实时检测的要求。针对这一问题，提出了一种基于场景视频的实时飞机检测网络 RPD（Realtime Planes Detection）。构建用于特征提取的轻量级卷积骨干网络 RPDNet4。设计了新的核心组件 CBL 模块（Conv（卷积）、BN（批量归一化）、ReLU（修正线性单元））来扩展神经网络中的感受野范围。通过添加可分离深度卷积来设计轻量级通道调整模块块，以减少模型的结构参数。GIou 损失的损失函数提高了网络训练的收敛速度。设计了四尺度预测模块和相邻尺度特征融合技术，融合不同抽象层次的相邻特征。此外，构建了一个从低到高的特征金字塔结构，以提高机场飞机小目标检测的准确性。实验结果表明，与 YOLOv3、Faster-RCNN 和 SSD 模型相比，RPD 模型的检测精度分别提高了 5.4%、7.1%和 23.6%；在模型参数方面，RPD 模型分别减少了 40.5%、33.7%和 80.2%；在检测速度方面，YOLOv3 为 8.4 fps，而 RPD 模型达到 13.6 fps，比 YOLOv3 快 61.9%。
