# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 2 Related Work  
### YOLOv7  
The main structure of the algorithm model of YOLOv7 [18] is relatively similar to its previous series, YOLOv5 [19]. This is primarily broken down into the following four components: input, Backbone, Neck, and Head.  
The Backbone framework serves as the foundation for signature acquisition within YOLOv7. It facilitates the initial extraction of features for target detection, culminating in the generation of feature layers. These extracted feature layers from the Backbone play a pivotal role in the subsequent network construction, earning them the nomenclature of \"effective feature layers\". Remarkably, the Backbone feature extraction network employed by YOLOv7 capitalizes on the E-ELAN module, characterized by a final stacking module comprising four branches. This configuration engenders a denser residual structure resulting from multiple stacks, a distinction that imparts simplicity to the optimization and permits accuracy enhancement via increased network depth.  
The YOLOv7 Neck module undertakes the critical task of performing feature fusion on the previously generated successful feature layers. This module represents a noteworthy evolution in the layer-extraction network of YOLOv7, strategically devised to address the challenges arising from varying target sizes within the realm of deep learning. Moreover, it effectively tackles the issue of image noise. Notably, YOLOv7 retains the framework of the Panet structure from its predecessor series. This not only extends the inherent characteristics of the architecture for improved synergy, but also involves an additional round of feature downsampling, thereby achieving comprehensive feature fusion.  
The vital components of YOLOv7, responsible for classification and regression, are collectively referred to as the YOLO Head. Notably, the Backbone and Feature Pyramid Network (FPN) now contribute improved effective feature layers. In the Head module, each feature layer is equipped with its corresponding parameters. The subsequent process involves mapping the feature map to a collection of feature points, followed by their amalgamation with feature frames. This strategic approach ensures that every prior frame corresponds to multiple feature channels, forming the fundamental premise of the YOLO Head. In essence, the YOLO Head evaluates the feature points, thereby establishing the association between previous frames and target entities. Analogous to its earlier iterations, YOLOv7 executes regression and classification through a 1 \(\times\) 1 convolution featuring decoupled Heads.  
In order to forecast object cases matching the previous frame, the entire YOLOv7 network performs the following tasks: input image processing; feature extraction; feature improvement; and prediction. Figure 1 depicts its particular network structure.  
This layer-by-layer transfer-type structure leads to the loss of information transfer between each layer. In addition, because of the fixation of the detection frame of the YOLOv7 model, it has certain defects for both small targets and multi-target detection with large size variations. Its own SPPCSPC module also has some shortcomings in both the speed and accuracy of processing information. Therefore, we designed a new network model structure with an improved SPPFCSPC module, as described in Section 3.