结合DenseNet与通道注意力机制的空对地目标检测算法

Air-to-Ground Target Detection Algorithm Based on DenseNet and Channel Attention Mechanism

空对地环境下成像视角单一,且需要依靠深层网络提供强特征表达能力。针对深层网络存在的计算量大、收敛速度慢等问题,在稠密连接网络(DenseNet)框架下,提出了一种用通道差异化表示的目标检测网络模型。首先,用DenseNet作为特征提取网络,并用较少的参数加深网络,以提高网络对目标的提取能力;其次,引入通道注意力机制,使网络更关注特征层中的有效特征通道,重新调整特征图;最后,用空对地目标检测数据进行了对比实验。结果表明,改进模型的平均精度均值比基于视觉几何组(VGG16)的单步多框检测算法高3.44个百分点。

In the air-to-ground environment,the imaging perspective is single,and it is necessary to rely on deep network to provide stronger feature representation capabilities.Aiming at the problems of large amount of calculation and slow convergence speed brought by deep network.Under the framework of densely connected network(DenseNet),a target detection network model expressed by channel differentiation is proposed.First,this article uses DenseNet as a feature extraction network,and uses fewer parameters to deepen the network to improve the ability to extract objects.Second,channel attention mechanism is introduced to make the network pay more attention to the effective feature channels in the feature layer and readjust the feature map.Finally,a comparative experiment is carried out by using the air-to-ground object detection data.The results show that the mean average precision of the improved model is 3.44 percentage points higher than that of single shot multibox detection algorithm based on visual geometry group(VGG16).