CM-YOLO: Context Modulated Representation Learning for Ship Detection

Ship detection is essential for both military and civilian applications. Existing ship detection methods focus on prominent offshore ships, paying less attention to complex nearshore ships, which are easily confused with the intricate background. Utilizing contextual information, such as location and shape, can enhance ship detection and classification in complex environments. In this article, we propose a context modulated representation learning-based detection method termed as CM-YOLO. It adopts the classical detector design framework, which includes the backbone, neck, and head. The input image is sequentially processed through these components to obtain the detection results. Our method specifically optimizes ship detection in complex scenarios. To achieve this, we propose a dual path context enhancement neck (DCEN) to extract contextual information for ship detection. The neck builds on the path augmentation feature pyramid network with the proposed dual path context enhancement (DCE) module, which is designed to enhance feature representations by incorporating high-level semantic information. It captures long-range dependencies across both channel and spatial dimensions while suppressing irrelevant features. Additionally, to enhance the scale-aware capability of the head for detecting multiscale ships in complex environments, we introduce the multicontext boosted (MCB) detection head. The MCB can flexibly adjust the receptive field and extracts relevant context for ships of various scales using multiple large-kernel convolutions. We conduct experiments on three commonly used ship datasets: Seaships7000, ShipRSImageNet, DIOR-ship, and HRSC2016. Experiment results demonstrate that CM-YOLO achieves excellent performance compared with other leading ship detection methods.