# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 2 Method
### Low-Light Image Enhancement Module (SCINet)  
Images captured under low-light conditions often suffer from reduced photon count, leading to an overall decline in image quality and the blurring of image features. To address this issue, we introduce an adaptive image enhancement network, SCINet. SCINet follows an unsupervised training paradigm and serves as a real-time enhancement network. The entire architecture consists of two primary components: an Illumination Estimation module and a Self-Calibrated Module [37]. Cascaded illumination learning blocks dynamically estimate the illumination distribution through a Self-Calibrated Module, aiming to restore brightness uniformity and improve the image signal-to-noise ratio in low-light scenarios. The structure of SCINet is illustrated in Figure 2.  
Figure 1: Structure diagram of Dark-YOLO model. The baseline model for Dark-YOLO is YOLOv8. The Dark-YOLO detection network is composed of three main components: Backbone, Neck, and Head. The Backbone utilizes the SCI, PSM, and CSPPF modules to enhance the quality of low-light images and extract key features. The Neck integrates a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) to achieve multi-scale feature representation. It also employs the D-RAMiT module to fuse spatial and channel features, enhancing the model’s ability to perceive both local and global features. The Head leverages convolutional layers and activation functions to further extract high-level features. The PSM module employs partial convolution along with an attention mechanism to focus on and extract critical features. The CSPPF module extracts and fuses features by employing a cross-overlapping strategy involving multiple max pooling layers and average pooling layers. The Spatial Self-Attention (SPSA) of D-RAMiT restores fine-grained details, while the Channel Self-Attention (CHSA) models inter-channel dependencies. This allows the model to infer object identities from limited local cues by leveraging global scene statistics. The Illumination Estimation process is a critical part of the SCI framework, involving the estimation of light intensity within an image, which aims to ensure consistency among the outputs generated by multiple cascaded basic blocks (i.e., illumination estimation modules). This estimation is performed either in grayscale or in specific channels after color space conversion. The objective of Illumination Estimation is to provide the necessary information for subsequent image enhancement, facilitating adjustments to image brightness and contrast. The Self-Calibrated Module is designed to support the learning process of illumination estimation. By employing a weight-sharing mechanism during the training phase, this module helps accelerate convergence. Within the SCINet framework, the output of the Self-Calibrated Module is added to the original input after each iteration and used as input for the subsequent illumination estimation. This process forms a feedback loop, as detailed in Equations (1)-(3). Among them, \(F(X^{t})\) is the illumination-estimation processing function for the \(t\)-th stage; \(G(X^{t})\) is the self-calibrated module for calibrating the input of the \(t\)-th stage. \(x^{t}\) represents the illumination estimation result at the \(t\)-th stage; \(y\) is the input low-illumination image; \(z^{t}\) is obtained by performing element-wise division between \(x^{t}\) and \(y\), reflecting the difference between the current illumination estimation and the input image; \(\kappa_{\theta}\) is the transformation function in the self-calibrated module; \(s^{t}\) is the calibration signal generated after the transformation function processes \(z^{t}\); \(v^{t}\) is the output of the self-calibrated module; H\({}_{\theta}\) is the transformation function in the illumination estimation module; \(u^{t}\) is the residual term generated after the transformation function processes \(v^{t}\); \(x^{t+1}\) is the illumination estimation result at the \(t+1\)-th stage. \[F(X^{t}):\left\{