# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 3 Methods  
Based on the YOLO11 architecture, YOLO-SAR addresses key limitations of the baseline model by enhancing its backbone, neck, and detection head components. The backbone integrates an illumination enhancement module, multiple convolutional layers, and a Spatial Pyramid Pooling Fast (SPPF) module. SCINet employs a cascaded illumination learning process and a weight-sharing mechanism to improve visual clarity in low-light images, thereby recovering fine-grained, multi-scale ship features. Furthermore, the standard convolutions are replaced with the receptive-field attention convolution (RFAConv) module,  
Figure 1: YOLO11 optimized network module diagram.  
which dynamically recalibrates intra-receptive-field feature weights to enhance global context modeling and compensate for limitations in capturing long-range dependencies. The SPPF module consolidates multi-scale features into a unified resolution, improving the detection of ships of varying sizes. In the neck section, the model adopts a hybrid of feature pyramid network (FPN) and Path Aggregation Network (PANet) to enable hierarchical feature fusion. The backbone outputs are first channel-aligned and then refined through the RFAConv module, which enhances salient feature representations using a dynamic receptive-field attention mechanism. Top-down and bottom-up fusion through upsampling and downsampling operations further integrates multi-scale features into robust fused representations, enhancing detection across different scales. The detection head adopts the Adaptively Spatial Feature Fusion (ASFF) module, which dynamically assigns spatial attention weights to resolve semantic conflicts during cross-scale feature aggregation. ASF adaptively modulates contributions from different layers, mitigating contour ambiguity and feature degradation in low-light conditions. Additionally, a decoupled anchor-based head separately optimizes bounding box regression and object classification, thereby improving both precision and robustness in challenging environments. Collectively, these architectural enhancements significantly strengthen the model's capability for reliable vessel detection under complex low-illumination scenarios.  
In low-illumination ship-detection tasks, the SCINet module utilizes pixel clues from low-light environments through abstract node interactions. By constructing abstract node comprehensions and feature representations, it effectively extracts low-light characteristics and enables the detector to capture ship features more accurately. The receptive field is introduced to restore the missing global context subsequently, thereby reducing the likelihood of missed detections of small ships. They are supposed to focus on the frame edge representations. Furthermore, the Adaptively Spatial Feature Fusion (ASFF) in the head adaptively fuses cross-scale cues, facilitating multi-scale learning and ensuring precise ship recognition. In conclusion, our improved YOLO11 framework consists of SCINet, RFAConv, and ASF modules. SCINet emphasizes low-contrast and low-saturation ship patterns. ASF dynamically readjusts their weights within the cascade, feature tuning in time and blemish filtering, which ensures robust detection. Its complete architecture is illustrated in Figure 2 and guarantees robust ship detection.