基于深度学习的轻量化目标检测算法

Lightweight target detection algorithm based on deep learning

深度卷积神经网络在各个领域都表现出很好的效果,与之伴随的是庞大的计算量和参数量。针对当前基于深度卷积神经网络的目标检测算法对计算资源需求太大和内存消耗严重的问题,提出一种高性能轻量化的网络模型。首先将Stem模块和ShuffleNet V2进行融合,提升网络特征提取能力,并利用融合后的网络对原始YOLOv5的骨干网络进行重构,显著降低了网络的计算量和内存占用,同时,引入可变形卷积以提升网络的检测性能。道路监控图像和VOC、COCO数据集测试结果表明,所提出的模型在保持检测精度的前提下,将参数量和模型尺寸降低了90%,计算量仅为原始模型的18%,实现了检测模型的轻量化,更有助于在计算资源有限和对实时性要求高的场景中部署。

Deep convolution neural networks have shown good results in various fields,accompanied by a huge amount of calculation and parameters.Aiming at the problems of high requirement of computational resources and serious memory consumption of the current deep convolution neural network based object detection algorithms,a high-performance lightweight network model is proposed.Firstly,Stem module and ShuffleNet V2 are fused to improve the network feature extraction capability,and the original YOLOv5 backbone network is reconstructed by the fused network,which significantly reduces the computational cost and memory consumption of the network.Meanwhile,deformable convolution is introduced to improve the detection performance of the network.Experimental results on the road monitoring images and VOC,COCO data sets show that the proposed model reduces the parameter and model size by 90%,and the calculation amount is only 18%of the original model,while the detection accuracy can be still maintained.The proposed lightweight detection model is more conducive to be deploied in the scenarios of limited computational resources and high real-time requirements.