  Multi-scale detection plays an important role in object detection models.
However, researchers usually feel blank on how to reasonably configure
detection heads combining multi-scale features at different input resolutions.
We find that there are different matching relationships between the object
distribution and the detection head at different input resolutions. Based on
the instructive findings, we propose a lightweight traffic object detection
network based on matching between detection head and object distribution,
termed as MHD-Net. It consists of three main parts. The first is the detection
head and object distribution matching strategy, which guides the rational
configuration of detection head, so as to leverage multi-scale features to
effectively detect objects at vastly different scales. The second is the
cross-scale detection head configuration guideline, which instructs to replace
multiple detection heads with only two detection heads possessing of rich
feature representations to achieve an excellent balance between detection
accuracy, model parameters, FLOPs and detection speed. The third is the
receptive field enlargement method, which combines the dilated convolution
module with shallow features of backbone to further improve the detection
accuracy at the cost of increasing model parameters very slightly. The proposed
model achieves more competitive performance than other models on BDD100K
dataset and our proposed ETFOD-v2 dataset. The code will be available.
