Dock detection method in remote sensing images based on improved YOLOv4

The dock target in remote sensing images has the characteristics of slender structure and direction arbitrarily. The general target detection algorithm based on the convolutional neural network cannot effectively obtain the direction information of the target, which cannot meet the actual demand of dock detection. This study designed a deep convolutional neural network architecture in any direction based on the YOLOv4 algorithm aimed at resolving the above problems. First, the multidimensional coordinate method was used to calibrate the dock target so that the network could contain the direction information of the target. Second, the loss function of the algorithm was optimized to make it suitable for directional target detection. Finally, an attention mechanism was introduced to enhance the extraction ability of the algorithm and further improve its detection accuracy. Two datasets of dock target detection from remote sensing images were selected for experiments, and the results showed that the improved YOLOv4 network was better than the other networks in the dock target detection task.