  One-stage algorithm have been widely used in target detection systems that
need to be trained with massive data. Most of them perform well both in
real-time and accuracy. However, due to their convolutional structure, they
need more computing power and greater memory consumption. Hence, we applied
pruning strategy to target detection networks to reduce the number of
parameters and the size of model. To demonstrate the practicality of the
pruning method, we select the YOLOv5 model for experiments and provide a data
set of outdoor obstacles to show the effect of model. In this specific data
set, in the best circumstances, the volume of the network model is reduced by
49.7% compared with the original model, and the reasoning time is reduced by
52.5%. Meanwhile, it also uses data processing methods to compensate for the
drop in accuracy caused by pruning.
