  Recently, Neural architecture search has achieved great success on
classification tasks for mobile devices. The backbone network for object
detection is usually obtained on the image classification task. However, the
architecture which is searched through the classification task is sub-optimal
because of the gap between the task of image and object detection. As while
work focuses on backbone network architecture search for mobile device object
detection is limited, mainly because the backbone always requires expensive
ImageNet pre-training. Accordingly, it is necessary to study the approach of
network architecture search for mobile device object detection without
expensive pre-training. In this work, we propose a mobile object detection
backbone network architecture search algorithm which is a kind of evolutionary
optimized method based on non-dominated sorting for NAS scenarios. It can
quickly search to obtain the backbone network architecture within certain
constraints. It better solves the problem of suboptimal linear combination
accuracy and computational cost. The proposed approach can search the backbone
networks with different depths, widths, or expansion sizes via a technique of
weight mapping, making it possible to use NAS for mobile devices detection
tasks a lot more efficiently. In our experiments, we verify the effectiveness
of the proposed approach on YoloX-Lite, a lightweight version of the target
detection framework. Under similar computational complexity, the accuracy of
the backbone network architecture we search for is 2.0% mAP higher than
MobileDet. Our improved backbone network can reduce the computational effort
while improving the accuracy of the object detection network. To prove its
effectiveness, a series of ablation studies have been carried out and the
working mechanism has been analyzed in detail.
