  Deep neural network (DNN) pruning has become a de facto component for
deploying on resource-constrained devices since it can reduce memory
requirements and computation costs during inference. In particular, channel
pruning gained more popularity due to its structured nature and direct savings
on general hardware. However, most existing pruning approaches utilize
importance measures that are not directly related to the task utility.
Moreover, few in the literature focus on visual detection models. To fill these
gaps, we propose a novel gradient-based saliency measure for visual detection
and use it to guide our channel pruning. Experiments on the KITTI and COCO
traffic datasets demonstrate our pruning method's efficacy and superiority over
state-of-the-art competing approaches. It can even achieve better performance
with fewer parameters than the original model. Our pruning also demonstrates
great potential in handling small-scale objects.
