Parallel Feature Pyramid Network for ObjectDetection

本文提出一种用于目标检测的新特征金字塔方法,利用SPP并行生成具有相似语义信息的不同尺寸特征,通过融合得到每层特征,特别适用于小目标检测,CVPR19的人体位姿文章参考了此方法。

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ECCV18

现在主流的一些检测方法通常使用一个网络来 生成通道数递增的特征,如SSD, 但是这样不同层语义信息差距较大, 会限制检测精度, 尤其是对小目标检测。 作者认为相较于提升深度, 提升网络的宽度更有效。

首先使用SPP(spatial pyramid pooling) 来生成不同分辨率的特征, 这些特征是并行生成的,可以认为这些不同尺寸的特征之间有相似的语义信息。 然后我们resize这些特征到相同尺寸, 进行融合, 得到特征金子塔的每个层特征。

 这个图是不同方法使用特征的方式, 其中(a)下是SSD的方式, (d)是文章提出的方法。

细节如下:

输入图片经过base network提取的特征大小设为:D*W*H。 经过SPP, 生成不同尺寸相同channel的特征:

其中第n个特征通道数为D, 分辨率为

我们继续使用bottleneck来进一步提取特征和降低channel,生成:

通道数均为:D/(N-1)。 分辨率不变。

然后用MSCA模块来融合F_HF_L。具体的:

p_1的生成为例, 首先将f^{(0)}_L降采样, f^{(2)}_L上采样,到f^{(1)}_L的尺寸, 然后通过skip连接将f^{(1)}_H与它们concat。

之后再通过一段卷积, 得到p_1

小结:

本文提出一种新的生成feature pyramid的方法,结合SPP, 通过并行的方式生成。

CVPR19的 deep high resolution那篇做人体位姿的文章应该就是参考了这篇, 使用并行架构。

### YOLO P2 Detection Layer Implementation and Usage In the context of object detection models like YOLO, different feature layers are used to detect objects at various scales. The P2 layer specifically refers to one such feature map that is part of a multi-scale prediction setup within architectures like those described in advanced versions of YOLO. The implementation details for incorporating or utilizing a P2 detection layer can be understood through several key aspects: #### Feature Pyramid Networks (FPN) A common approach involves using FPNs where features from multiple levels of the network pyramid are combined[^1]. This allows detectors to leverage high-resolution information as well as semantically strong but lower resolution data. In this scheme, P2 typically represents an earlier stage with higher spatial dimensions compared to deeper stages like P3, P4, etc., which have progressively reduced resolutions. For instance, when configuring `yolov11-C3k2_SCConv.yaml`, settings related to backbone architecture would define how these pyramidal features are extracted and processed before being passed on to heads responsible for making predictions based on them. #### Multi-Scale Predictions To implement multi-scale predictions effectively, including support for P2-level detections, modifications might need to occur both during training configuration and inference pipeline design. Specifically, - **Training Configuration**: Adjustments may include specifying input sizes suitable for capturing finer-grained structures while ensuring computational efficiency. - **Inference Pipeline Design**: During deployment, it's crucial to ensure all necessary components—such as non-max suppression—are tuned appropriately so they handle outputs across varying scales correctly without introducing significant latency issues. An example snippet demonstrating adjustments made within Python code could look something similar to below: ```python from yolox.models import YOLOXHead class CustomYOLOX(YOLOX): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Add custom head supporting additional scale e.g., p2 self.head_p2 = YOLOXHead(num_classes=80, width=self.width/2.) def forward(self, x): ... out.append(self.head_p2(features['p2'])) return tuple(out) ``` This modification ensures that alongside existing detection capabilities, there’s now explicit handling for smaller objects via enhanced sensitivity provided by integrating P2 level features into final output generation process[^3]. #### Simplified SKNet Method Integration When considering enhancements aimed at improving performance further, techniques inspired by simplified SKNet methods offer ways to dynamically adjust contributions between parallel branches operating over distinct receptive fields. By applying global average pooling followed by softmax operations, weights assigned to each branch allow adaptive fusion tailored towards specific contexts encountered throughout images under analysis[^4]: \[ \text{Output} = \beta_1Y_1 + \beta_2Y_2 \] where \( Y_1 \) and \( Y_2 \) represent responses derived separately yet concurrently; meanwhile, \( \beta_1 \) and \( \beta_2 \) denote learned coefficients guiding combination strategy applied post-processing step involving channel-wise refinement. --related questions-- 1. How does integrating P2 affect overall model accuracy versus speed trade-offs? 2. What considerations should be taken into account regarding memory consumption when adding more detection layers? 3. Can you provide examples comparing single-scale vs multi-scale approaches focusing particularly around small object recognition improvements? 4. Are there any particular challenges associated with fine-tuning pre-trained networks intended originally only up until certain depths say till P3 instead extending down even closer toward original inputs i.e., reaching P2?
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