一种融合注意力机制的无人机目标分割算法

A Drone Object Segmentation Algorithm Integrating Attention Mechanism

由于低空空域无人机具有尺寸小、飞行灵活等特点,给视觉检测非法入侵无人机带来困难,提出一种融合注意力机制的低空无人机目标分割算法,命名为Rep-YOLACT(re-parameterization-you only look at coefficients network),首先采用RepVGG(rep visual geometry group)网络改进YOLACT网络中ResNet(residual network)主干,增强网络的特征提取能力,同时在主干特征提取网络输出的3个特征层后添加CBAM(convolutional block attention module)注意力模块,从而进一步高效利用特征层的信息.分别在FL-drones(flying drones dataset)数据集和MUD(multiscale unmanned aerial vehicle dataset)数据集上进行实验,结果表明,在FL-drones数据集上,所提出的Rep-YOLACT算法相比于YOLACT算法在掩膜AP(average precision)和掩膜AR(average recall)上分别提升了0.3%和11.7%,在MUD数据集上,所提出的Rep-YOLACT算法相比于YOLACT算法掩膜AP和预测框AR上提升了2.3%和5%,能够很好地完成无人机分割任务,其分割精度也高于其它主流分割算法.

Low-altitude airspace drones are characterized by small size and flexible flight,which brings difficulties to visual detection of trespassing drones.The low-altitude drone object segmentation algorithm incorporating an attention mechanism named Rep-YOLACT(re-parameterization-you only look at coefficients network)is proposed,which is first used with RepVGG(re-parameterization visual geometry group)networks to improve ResNet(residual network)backbone in YOLACT and enhance the feature extraction capability of the network.Meanwhile,CBAM(convolutional block attention module)is added after the three feature layers output from the backbone feature extraction network,so as to further utilize the information of the feature layers efficiently.Experiments are conducted on FL-drones(flying drones dataset)and MUD(multiscale unmanned aerial vehicle dataset),respectively.The results show that the proposed Rep-YOLACT algorithm improves mask AP(average precision)and mask AR(average recall)by 0.3%and 11.7%,respectively,compared with YOLACT algorithm on FL-drones.The proposed Rep-YOLACT algorithm improves 2.3%and 5%on mask AP and prediction frame AR compared to YOLACT algorithm,which can perform the drone segmentation task well and its segmentation accuracy is higher than other mainstream segmentation algorithms.