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 paper, 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 Module (DCE), 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 multi-scale ships in complex environments, we introduce the Multi-Context Boosted Detection Head (MCB). 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.

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

Optical imagery ship detection has achieved significant developments recently. However, accurate detection in complex scenes and for different scale ships remain vital challenges. To solve the above issues, in this paper, we propose the Asymmetric Aggregation Feature Pyramid Network (A2FPN), incorporating top-down semantic aggregation and bottom-up detail enhancement to propagate semantic and detailed information across different feature levels. In particular, the higher-level hierarchical features propagate global semantic information to the lower-level hierarchical features, successively enhancing the discriminative ability of each level of hierarchical features. After that, the lower-level hierarchical features with abundant semantic information are also aggregated successively to the higher-level hierarchical features through the augmentation path, enriching the details of each level of hierarchical features. Considering the real-time requirements of ship detection, we replace the original path aggregation feature pyramid network of YOLOX with the proposed A2FPN and develop a ship detection model termed Asymmetric Aggregation Network (A2Net). Extensive experiments are performed on the three commonly used ship detection datasets, ShipRSImageNet, Seaships7000 and HRSC2016. Quantitative and qualitative results demonstrate that A2Net outperforms the state-of-the-art methods.