## 2 Related Work
### Feature Fusion Structure  
The effectiveness of object detection models largely depends on their ability to combine features from different layers of the neural network. The Feature Pyramid Network (FPN [11]) and Path Aggregation Network (PANet) [43] focus on the integration of low-level details and high-level semantic information. These support the more efficient use and comprehensive combination of features from various depths within the network, and they have introduced more complex feature fusion methods that significantly improve the detection of objects across scales and complexity.  
A feature fusion network is used to combine feature maps at different levels. The FPN processes feature maps at different scales by creating feature pyramids. It allows the network to process objects of different scales simultaneously, thus improving the performance of the detector. However, when a FPN performs feature fusion on multi-scale feature maps, problems such as the loss of small object information remain. To address the issue of minor information loss inherent in traditional Feature Pyramid Networks (FPNs) [7], a novel Search Feature Pyramid Network (SFPN) was developed [44]. This approach utilizes a neural network structure search methodology, enabling the automatic selection of optimal scale configurations, layer depth, and feature fusion techniques for enhanced performance.  
To solve the problem of small information loss, a Search Feature Pyramid Network (SFPN [24]) is designed in a FPN, which automatically selects the scale, number of layers, and feature fusion method that uses the neural network structure search method. To solve the problem of low FPN accuracy, a path aggregation network (PANet [43]) based on adaptive feature pooling and feature weighting was proposed. Adaptive feature pooling and feature weighting in feature maps at different levels can better capture the features of small objects. A bidirectional feature pyramid network (BiFPN [45]) that provides feature fusion paths in both top-down and bottom-up directions was proposed by [PERSON] et al. This structure achieves adaptive feature fusion across different scales and levels.  
Adaptive feature pooling and feature weighting in feature maps at different levels can better capture the features of small objects [45]. The bidirectional feature pyramid network (BiFPN) proposed in [45] offers feature fusion paths in both top-down and bottom-up directions. With this structure, BiFPN, adaptive feature fusion between different scales and levels was achieved.  
Traditionally, features at different levels carry spatial information about objects of various sizes. Larger features include low-dimensional texture details and the positions of smaller objects. In contrast, smaller features contain high-dimensional information and the positions of larger objects. The original idea behind Private Pyramid Networks (FPNs) is that these various pieces of information can improve network performance through mutual aid. A FPN provides an effective architectural design to combine multi-scale features through cross-scale connections and information exchange, thereby improving the detection accuracy of objects of various sizes.