Remote Sensing Image Ship Detection Based on Dynamic Adjusting Labels Strategy

Remote sensing ship detection is a hotspot in computer vision, which is vital in both military and civilian fields. Nevertheless, the arbitrary orientation and dense arrangement of ship targets impose significant challenges in high-precision detection. Although research on this problem has progressed, high-precision detection is still limited by angular prediction accuracy. To tackle the issue, we start with angle prediction and propose a Dynamic Adjusting Labels (DAL) strategy based on Binary Coded Label (BCL). DAL strategy dynamically adjusts the ground-truth coded labels in the training process to guide angle coding for tendency learning. This strengthens the coupling between the angle coding bits and improves the performance of small granularity intervals. Due to the angle interval granularity difference, the learning difficulty of the coding layers and the convergence speed vary significantly. Aiming at this problem, we add a gradient truncation mechanism to each coding bit loss. The mechanism can effectively balance the coding layers' learning strength and enhance the model's training emphasis on coding bits corresponding to small granularity intervals, avoiding the effect of coding bits learning imbalance on angle prediction. Extensive experiments based on three public datasets demonstrate our method's superiority in high-precision detection and state-of-the-art performance. Our code is available at https://github.com/lirunsheng2008/SDALS.