Small Object Detection by DETR via Information Augmentation and Adaptive
  Feature Fusion
Ji Huang Hui Wang
The main challenge for small object detection algorithms is to ensure
accuracy while pursuing real-time performance. The RT-DETR model performs well
in real-time object detection, but performs poorly in small object detection
accuracy. In order to compensate for the shortcomings of the RT-DETR model in
small object detection, two key improvements are proposed in this study.
Firstly, The RT-DETR utilises a Transformer that receives input solely from the
final layer of Backbone features. This means that the Transformer's input only
receives semantic information from the highest level of abstraction in the Deep
Network, and ignores detailed information such as edges, texture or color
gradients that are critical to the location of small objects at lower levels of
abstraction. Including only deep features can introduce additional background
noise. This can have a negative impact on the accuracy of small object
detection. To address this issue, we propose the fine-grained path augmentation
method. This method helps to locate small objects more accurately by providing
detailed information to the deep network. So, the input to the transformer
contains both semantic and detailed information. Secondly, In RT-DETR, the
decoder takes feature maps of different levels as input after concatenating
them with equal weight. However, this operation is not effective in dealing
with the complex relationship of multi-scale information captured by feature
maps of different sizes. Therefore, we propose an adaptive feature fusion
algorithm that assigns learnable parameters to each feature map from different
levels. This allows the model to adaptively fuse feature maps from different
levels and effectively integrate feature information from different scales.
This enhances the model's ability to capture object features at different
scales, thereby improving the accuracy of detecting small objects.