With the ongoing development of artificial intelligence technology, deep learning methods have become increasingly important role in the field of ship detection. However, the false alarms and missed detections that appear in deep learning algorithms hinder the application of technology in the field of ship detection. Although the classical deep learning methods can effectively deal with a single-background sea surface, the classical models can easily yield false alarms on shore when faced with data under complex backgrounds. In custom training, the model often tends to overly emphasize some salient features, which leads to feature overfitting. Detection can easily be missed when these salient features change. In the process of forward propagation of the model to the input, different network layers in the model generate corresponding mappings or feature maps from the input. Fully utilizing the semantic and spatial information of the feature maps is an effective way to reduce false alarms and missed detections. Compared with the traditional model, our proposed Feature Map Reinforcement Network (FMRNnet) can fully utilize feature maps to generate adaptive feature map masks and water–land segmentation masks. This method ultimately reduces false alarms and missed detections by avoiding feature overfitting of the model and weakening the effects caused by complex backgrounds. In FMRNnet, we design the Self Feature-map Mask Module (SFMM), which can selectively utilize the feature map through the attention mechanism for generating an adaptive mask. The mask prevents the model from focusing on a single feature point, which prevents feature overfitting. We also propose a Feature-map Sea-Land Segmentation Module (FSSM) that is parallel to SFMM. It reduces the false alarms of ship targets appearing in the land area by introducing the fusion between the water-land segmentation mask and the feature map. The experimental results, when compared with SOTA algorithms on publicly available datasets, show that the performance of the proposed method in this study is excellent and outperforms that of other SOTA algorithms. After FMRNet is added, the 10-fold average mAP value of the detection algorithm ROI trans largely improves. This enhancement increases the mean value of baseline mAP from 86.1% to 90.8%, which surpasses that of other SOTA algorithms. Benefiting from the adaptive mask, the mAP value of the model including the SFMM module is 90.4%, which achieves a 4.2% improvement over the baseline. Owing to the priori knowledge learned from the water-land distribution, FSSM improves the precision and recall of the model, which results in a MAP value of 86.4%. For the task of ship detection, we propose a novel backbone network, that is, FMRNet based on Resnet. Our proposed SFMM module enables the model to discern the target from multiple features for avoiding the overfitting of salient features. We design the FSSM module to reduce the false alarms caused by complex backgrounds. Suppressing the non-water surface area reduces the confidence level of targets appearing in non-water surface. FSSM achieves the purpose of removing unreasonable false alarms while improving the accuracy of the model.

remote sensing imaging;artificial intelligence;ship detection;neural networks;feature maps;network reinforcement;rotational object detection;overfitting suppression