FCOS-Based Anchor-Free Ship Detection Method for Consumer Electronic UAV Systems

The emergence of consumer electronic Unmanned Aerial Vehicle (UAV) systems has brought innovation to the field of ship inspection. Traditional ship detection is carried out through traditional target recognition methods, but the efficiency and accuracy cannot meet the requirements. With the major breakthrough in the resolution of remote sensing images, it has become possible to use UAV to capture remote sensing images to detect ships. In this paper, object detection technology based on deep learning is used to improve the current detection methods and achieve accurate ship target detection. We propose an anchor-free detection method based on FCOS to reduce model hyperparameters. Meanwhile, a positive and negative sample selection method is put forth based on attention mechanism feature fusion and self-adaptation to enhance the fusion expression of features and improve the efficiency of sample selection, therefore improving the accuracy of the model. Experiments demonstrate notable progress in the detection accuracy from the proposed method, especially when small ship targets are concerned. Compared with Faster R-CNN and R3Det, the method introduced in this paper needs fewer hyperparameters, while achieving higher detection accuracy, with AP50 reaching 83.90%.