An Efficient Enhanced-YOLOv5 Algorithm for Multi-scale Ship Detection

Ship detection has gained considerable attentions from industry and academia. However, due to the diverse range of ship types and complex marine environments, multi-scale ship detection suffers from great challenges such as low detection accuracy and so on. To solve the above issues, we propose an efficient enhanced-YOLOv5 algorithm for multi-scale ship detection. Specifically, to dynamically extract two-dimensional features, we design a MetaAconC-inspired adaptive spatial-channel attention module for reducing the impact of complex marine environments on large-scale ships. In addition, we construct a gradient-refined bounding box regression module to enhance the sensitivity of loss function gradient and strengthen the feature learning ability, which can relieve the issue of uneven horizontal and vertical features in small-scale ships. Finally, a Taylor expansion-based classification module is established which increases the feedback contribution of gradient by adjusting the first polynomial coefficient vertically, and improves the detection performance of the model on few sample ship objects. Extensive experimental results confirm the effectiveness of the proposed method.