Abstract
Abstract A method is provided to resolve the issues of false recognition and lower recall of the you only look once version 4 (YOLOv4) algorithm in the aircraft targets prediction of remote sensing imagery. The method is based on the YOLOv4. According to the characteristics of small size and intensive arrangement of aircraft targets in remote sensing images, the backbone is mended to boost the prediction capacity of smaller targets. To strengthen the transmission and reuse of network features, the connection mode of the residual module in YOLOv4 is changed to the dense connection mode, and the five residual blocks in the backbone network are modified to the corresponding dense connection blocks to reduce the network layers and model redundancy. The experiment results reveal that compared with the YOLOv4, our method can enlarge targets features in smaller image regions, thereby availably increasing the prediction precision of aircraft targets.
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