复杂场景下多尺度船舶实时检测方法

船舶检测在军事侦察、海上目标跟踪、海上交通管制等任务中发挥着重要作用。然而，受船舶外形尺度多变和复杂海面背景的影响，在复杂海面上检测多尺度船舶仍然是一个挑战。针对此难题，提出了一种基于多层信息交互融合和注意力机制的 YOLOv4 改进方法。该方法主要通过多层信息交互融合（multi-layer information interactive fusion，MLIF）模块和多注意感受野（multi-attention receptive field，MARF）模块构建一个双向细粒度特征金字塔。其中，MLIF模块用于融合不同尺度的特征，不仅能将深层的高级语义特征串联在一起，而且将较浅层的丰富特征进行重塑；MARF由感受野模块（receptive field block，RFB）与注意力机制模块组成，能有效地强调重要特征并抑制冗余特征。此外，为了进一步评估提出方法的性能，在新加坡海事数据集（Singapore maritime dataset，SMD）上进行了实验。实验结果表明，所提方法能有效地解决复杂海洋环境下多尺度船舶检测的难题，且同时满足了实时需求。

A real-time detection method for multi-scale ships in complex scenes

Ship detection plays an important role in tasks such as military reconnaissance, maritime target tracking, and maritime traffic control.However, due to the influence of variable sizes of ships and complex background of sea surface, detecting multi-scale ships remains a challenge in complex sea surfaces.To solve this problem, an improved YOLOv4 method based on multi-layers information interactive fusion and attention mechanism was proposed.Multi-layers information interactive fusion (MLIF) and multi-attention receptive field (MARF) were applied and combined reasonably to build a bidirectional fine-grained feature pyramid.MLIF was used to fuse feature of different scales, which not only concatenated high-level semantic features from deep layers, but also reshaped richer features from shallower layers.MARF consisted of receptive field block (RFB) and attention mechanism module, which effectively emphasized the important features and suppressed unnecessary ones.In addition, to further evaluate the performance of the proposed method, experiments were carried out on Singapore maritime dataset (SMD).The results illustrate that the method proposed can effectively solve the problem of difficult detection of multi-scale ships in complex marine environment, and meet the real-time requirements at the same time.