  As human-machine interaction continues to evolve, the capacity for
environmental perception is becoming increasingly crucial. Integrating the two
most common types of sensory data, images, and point clouds, can enhance
detection accuracy. However, currently, no model exists that can simultaneously
detect an object's position in both point clouds and images and ascertain their
corresponding relationship. This information is invaluable for human-machine
interactions, offering new possibilities for their enhancement. In light of
this, this paper introduces an end-to-end Consistency Object Detection (COD)
algorithm framework that requires only a single forward inference to
simultaneously obtain an object's position in both point clouds and images and
establish their correlation. Furthermore, to assess the accuracy of the object
correlation between point clouds and images, this paper proposes a new
evaluation metric, Consistency Precision (CP). To verify the effectiveness of
the proposed framework, an extensive set of experiments has been conducted on
the KITTI and DAIR-V2X datasets. The study also explored how the proposed
consistency detection method performs on images when the calibration parameters
between images and point clouds are disturbed, compared to existing
post-processing methods. The experimental results demonstrate that the proposed
method exhibits excellent detection performance and robustness, achieving
end-to-end consistency detection. The source code will be made publicly
available at https://github.com/xifen523/COD.
