关键词: 复杂场景, 深度表示, 广义交并比损失, 焦点损失, 损失权重

Abstract:

Drone detection in complex ground background is a difficult problem of “Low and slow” target detection. For the disturbance of environmental objects and the small target of the drone, the accuracy of the target detection algorithm of the drone is low. A method for detecting target in complex scene based on depth representation is proposed. For the problem of inaccurate detection of the target position of the drone, the generalized cross-comparison ratio is used to measure the deviation between the real position of the target and the candidate target position. For the problem that the learning effect is poor due to the imbalance of positive and negative samples and the easy to divide the sample, the loss contribution of the negative sample and the easy-to-sort sample is reduced by the modulation factor of the focus loss. The weight of position loss and category loss is adjusted to improve positional accuracy. In addition, to verify performance, a drone data set is created. Experiments show that the algorithm in this paper is 20.04% higher than the YOLOv3 in the drone data, and the detection accuracy is better than the SSD and Retinanet on the PASCAL VOC.

Key words: complex scene, depth representation, GIoU loss, focus loss, loss weight