Cross-level Attention and Ratio Consistency Network for Ship Detection
Biaohua Ye, Tong Qin, Huajun Zhou, Jianhuang Lai, Xiaohua Xie · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
In ship detection task, target objects with extreme aspect ratios are common in practical applications. However, existing ship detection methods seldom make efforts to tackle this issue. In this paper, we present a novel Cross-level Attention and Ratio Consistency (CARC) Network for ship detection. First, we propose a Cross-Level Attention (CLA) module to generate attention signals by integrating information from both higher and lower level features. Specifically, for each feature, we calculate its similarity with features from adjacent levels. These similarities are utilized as weights to enhance the channels that consist of different-level information. By fusing multi-level information, a channel-wise attention vector is generated to enhance the learned representations in the base feature. Second, we propose a Ratio Consistency loss that promotes the networks to localize the ships with more accurate aspect ratios. Existing ship detection methods have different sensitiveness to width and height predictions, significantly increasing the learning difficulty for localizing target ships. We append an auxiliary supervision signal to the detection head in our method. This supervision signal measures the error between the predicted and the ground truth aspect ratios of target ships. Experiment results show that our model achieves significant performance gains compared to existing methods.