论文标题
基于Yolov5的通道修剪的深度学习方法，用于快速准确的户外障碍检测
Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection
论文作者
论文摘要
一阶段算法已被广泛用于需要使用大量数据训练的目标检测系统。他们中的大多数都以实时和准确的态度表现良好。但是，由于其卷积结构，他们需要更多的计算能力和更多的内存消耗。因此，我们将修剪策略应用于靶向检测网络以减少参数数量和模型的大小。为了证明修剪方法的实用性，我们为实验选择了Yolov5模型，并提供了室外障碍的数据集以显示模型的效果。在最佳情况下，在此特定数据集中，与原始模型相比，网络模型的体积减少了49.7％，并且推理时间减少了52.5％。同时，它还使用数据处理方法来补偿修剪导致的准确性下降。
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.
