关键词: 深度可分离卷积, 分组卷积, DS-YOLO模型, 通道特征变换, 多尺度预测

Abstract:

In order to solve the problem that the traditional feature extraction method is insufficient in aircraft detection accuracy and real-time in remote sensing images, two improvements are proposed based on the YOLOv3-tiny network in terms of accuracy improvement. The improvement point one： the way to extract image feature points from the network improved to use packet convolution, that is, an image is divided into three channels for convolution operations, combined with channel feature transformation to enhance the semantic association between channels. Improvement point two： add a scale detection to the deep feature of the network, and perform the fusion prediction between the upsampling and the shallow feature map. Introducing deep separable convolution in the aspect of speed increase insteads of traditional convolution to reduce the amount of parameter calculation and achieve model weight reduction. According to the improved network, a modified convolutional neural network DS-YOLO（Depthwise Separable YOLO） with 33 convolutional layers is proposed. The improved network is trained on the self-made remote sensing aircraft image to select the optimal weight. It is used to test and analyze low-quality test sets such as small targets, high exposure, and background interference. Experiments show that the improved algorithm improves the accuracy of the test set by 14.1%, the recall rate by 16.8%, and the detection of low-quality remote sensing aircraft images.

Key words: depth separable convolution, packet convolution, DS-YOLO model, channel feature transformation, multi-scale prediction