# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 3 Proposed Method
### Filter Importance Evaluation  
In this section, a comprehensive method is introduced to assess the significance of each filter within the convolutional layers of an object detection model. The primary aim of this evaluation is to understand the roles and contributions of different filters within the convolutional architecture, an essential aspect that drives the overall performance of the model. An evaluation is commenced by using a trained model and inputting an image containing the object for detection. As the input image contains the GT detection box's information, the precise location of the object can be located within the image and can serve as the basis for further analysis. Following the input process, feature maps are obtained from each convolution layer. Feature maps contain filter responses within a particular  
Figure 5: Structure of YOLOv4 with SA (incorporated into backbone and neck).  
Figure 6: Structure of YOLOv7 with SA (incorporated into backbone and neck).  
layer, which provide intricate operations of the convolutional layers, allowing us to discern the importance of individual filters. To assess filter importance systematically, the feature maps are divided into two distinct regions, foreground and background, leveraging the GT detection box information for a clear differentiation between the object of interest and its surroundings.  
With the regions defined, L2-norm values are calculated for both the foreground and background areas. Then, the absolute difference is computed in the L2-norm values between these regions, as it provides a quantitative measure of the filter's significance. A larger difference in the L2-norm values signifies the filter's pronounced role in distinguishing features. A significantly higher L2-norm value for the foreground region indicates that the filter excels at extracting features related to either the object or its surroundings. Such filters are regarded as highly relevant for the object detection task. By doing so, they can be ranked in terms of importance. This ranking not only provides insights into filter dynamics but also facilitates informed decisions regarding filter pruning and other optimization techniques, all designed to enhance the model's performance through fine-tuning without unnecessary complexity.  
The entire process leads to a more refined understanding of the model, which is based on an empirical analysis of the filters' roles. In this study, Algorithm 1 is proposed to outline the specific filter-pruning algorithm used, offering a clear pathway to achieve the described objectives.  
```
Input: model, featureMaps, groundTruthBox, inputImage Output: optimizedModel  foreach (layer in convolutionLayers(model)) {  featureMaps = extractFeatureMaps(layer, inputImage)  foregroundRegion = getRegion(featureMaps, groundTruthBox)  backgroundRegion = getRegion(featureMaps, complementOf(groundTruthBox))  L2 NormForeground = calculateL2 Norm(foregroundRegion)  L2 NormBackground = calculateL2 Norm(backgroundRegion)  difference = abs(L2 NormForeground - L2 NormBackground) if (difference is significantly large) {  markFilterAsImportant(layer) } }  rankedFilters = rankFiltersBasedOnImportance(convolutionLayers(model)) optimizedModel = applyOptimizations(model, rankedFilters) return optimizedModel
```  
**Algorithm 1** Filter evaluation using attention map