Adaptive Receptive Field Enhancement Network Based on Attention Mechanism for Detecting the Small Target in the Aerial Image

To address the problem of insufficient semantic feature information caused by small objects in aerial images, an adaptive receptive field enhancement network based on attention mechanism suitable for aerial scene is proposed. First, to make the model more suitable for deployment on UAV platforms with limited resources, the receptive field block is reconstructed by a series branch with feature multiplexing, which can make the output feature map of the front branch pass through the convolutional layer of the back branch for further feature extraction, improving the utilization of convolutional resources. Second, to expand the receptive field with as little loss of local contextual information as possible, Kronecker convolution is employed in the receptive field block branch for feature extraction, which can augment the original image covered by a single pixel on the feature map, expand its global semantic information and local contextual information, and improve the integrity of object extraction for small targets. Last, to solve the problem of a fixed receptive field size of neurons at each layer in the network caused by direct aggregation feature maps, the selective convolutional module based on attention mechanism is added to the network in this paper so that neurons at each layer in the network can adaptively adjust the receptive field size, so as to make the different sizes of the detector head to play the best detection ability. In this paper, experiments are carried out on a homemade aerial photography small target dataset to verify the effectiveness of the adaptive field enhancement network. The experimental results show that the adaptive field enhancement network proposed in this paper can effectively improve the detection accuracy of the algorithm on targets with low resolution and insufficient semantic feature information in aerial images and is more suitable for use in aerial scene.