2D支柱特征金字塔网络

### 2D Pillar Feature Pyramid Network (FPN) in Computer Vision In the context of object detection and image processing, a **Feature Pyramid Network (FPN)** is an architecture designed to handle multi-scale objects effectively by fusing features from different layers within a convolutional neural network. For two-dimensional data such as images, FPNs enhance performance through hierarchical feature extraction at multiple scales. A typical implementation involves constructing a top-down pathway with lateral connections that merge high-resolution semantic information from lower-level layers with coarse but semantically strong higher-level representations[^1]. This design allows models like Faster R-CNN or RetinaNet to detect both small and large objects more accurately than single-scale approaches. For applications specifically involving pillars—discretized vertical columns representing local regions—the concept can be extended into what might be termed "Pillar-based FPN". In this variant: - The input space gets divided into uniform grids forming individual pillars. - Each pillar aggregates point cloud data along its height dimension while maintaining spatial structure across width and depth dimensions. - Features extracted per pillar are then processed via standard CNN operations before being fed into an FPN-like mechanism where inter-layer fusion occurs. This approach has been particularly beneficial when dealing with LiDAR sensor inputs for autonomous driving scenarios; however, adapting similar principles directly onto traditional RGB imagery could offer advantages even outside specialized domains[^2]. #### Example Code Snippet Demonstrating Basic Structure Below demonstrates how one may implement parts of a simple 2D-PFN using PyTorch: ```python import torch.nn as nn class Simple2DPFN(nn.Module): def __init__(self, num_channels=64): super(Simple2DPFN, self).__init__() # Define backbone layers here... self.backbone = ... # Lateral convolutions for each level self.laterals = nn.ModuleList([ nn.Conv2d(num_channels * i, num_channels, kernel_size=1) for i in range(1, 5)]) # Top down path aggregation blocks self.aggregations = nn.ModuleList([ nn.ConvTranspose2d(num_channels, num_channels, stride=2, kernel_size=3, padding=1) for _ in range(4)]) def forward(self, x): c2, c3, c4, c5 = self.backbone(x) p5 = self.laterals[-1](c5) p4 = self.aggregations[0](p5) + self.laterals[-2](c4) p3 = self.aggregations[1](p4) + self.laterals[-3](c3) p2 = self.aggregations[2](p3) + self.laterals[-4](c2) return [p2, p3, p4, p5] model = Simple2DPFN() print(model) ```