An Effective Instance-Level Contrastive Training Strategy for Ship Detection in SAR Images
Yilong Lv, Min Li, Yujie He
Abstract
Existing ship detection approaches in SAR images often suffer from inadequate learning of the detector and sub-optimal detection performance. To this end, Based on self-supervised contrastive learning, this letter consider using the relationship between samples to develop a more effective training strategy. First, the Instance-based RoI encode head is proposed, named InsRen head, a simple yet effective network structure. Its purpose is to encode the samples into a contrastive feature space, facilitating the measurement of contrastive learning. Furthermore, to adapt contrastive learning to ship detection, we have redefined some basic terms, such as query, positive key, and negative key, which can help the model build the training pipeline. Finally, we design the Instance-based Contrastive loss that does not require label supervision, named InsCon loss. With the penalty of the InsCon loss, the queries and positive key can learn more similar representations in the contrastive feature space. Simultaneously, the query and negative key are as far away as possible to increase the difference. With the help of InsRen head and InsCon loss, the training of the detection model is more effective. Experimental results demonstrate the superiority of our method.