# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 3 Methods
### SCINet Module  
To address the challenges of poor discriminability of multi-scale features and significant spatio-temporal fragmentation in low-illumination scenarios, this framework introduces a Self-Calibrated Illumination Network (SCINet). SCINet is a deep learning model that integrates spatial and channel representations; it adaptively enhances multi-scale feature perception in low-illumination scenes marked by low saturation, low contrast, and weak discriminability. Built from several convolutional and deconvolutional layers, SCINet consists of two main components: an illumination learning module and a self-calibration module, which are described in detail below in Figure 3.  
The illumination learning module gradually refines illumination estimation via residual learning, capturing the residual between illumination and the low-illumination image, and in doing so, this approach reduces computational costs while stabilizing model performance. Employing the Retinex formulation, a low-illumination observation is modeled as the element-wise product of a clean image and its illumination map. Learnable mapping is then introduced to track illumination variations progressively, forming a bidirectional mapping that adapts dynamically to changing lighting and achieves precise image enhance  
Figure 3: SCINet module architecture.  
Figure 2: Architecture of the YOLO-SAR network. The blue module (Conv) denotes standard convolution used for basic feature extraction; light blue (C3K2) for feature refinement; purple (C2 PSA) for enhancing feature representations; black (SPPF) for multi-scale context modeling via spatial pyramid pooling; green (Upsample) for resolution recovery; and orange (Concat) for multi-layer feature fusion. The modules highlighted by red dashed rectangles—SCINet, RFAConv, and ASF_Detect—are the key improvements proposed in this study.  
ment. In Equation (1), \(\mathrm{u}^{\mathrm{t}}\) and \(\mathrm{x}^{\mathrm{t}}\) denote the residual term and the illumination at stage \(\mathrm{t}\), respectively. \(\mathrm{H}_{\mathrm{m}}\) represents the learnable mapping function.  
\[\mathrm{F}\big{(}x^{t}\big{)}:\left\{\begin{aligned} & u^{\mathrm{t}}=\mathrm{H}_{\mathrm{m}}( \mathrm{x}^{\mathrm{t}}),\mathrm{x}^{0}=y,\\ & x^{+1}=\mathrm{x}^{\mathrm{t}}+\mathrm{u}^{\mathrm{t}},\end{aligned}\right. \tag{1}\]  
As for low-illumination observations, the self-calibration module constrains each stage's output by defining an unsupervised training loss. It also reduces the model's computational cost and endows it with adaptability to diverse scenarios. This can be found in Equation (2).  
\[\mathrm{G}(\mathrm{x}^{\mathrm{t}}):\left\{\begin{aligned} & z^{t}=y^{\otimes x^{t}},\\ & s^{t}=K_{m}\big{(}z^{t}\big{)},\\ & v^{t}=y+s^{t},\end{aligned}\right. \tag{2}\]  
In Equation (2), \(v^{t}\) denotes the calibrated input to the next stage, and \(K_{m}\) represents a learnable parametric operator. On this basis, the input to the illumination-learning process becomes this calibrated output, and the basic unit of the illumination-optimization process can be expressed as Equation (3).  
\[\mathrm{F}\big{(}x^{t}\big{)}\rightarrow\mathrm{F}\big{(}G\big{(}x^{t}\big{)} \big{)}, \tag{3}\]