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).