基于改进YOLOv3算法的交通场景目标检测

Object Detection Based on Improved YOLOv3 Algorithm in Traffic Scenes

目标检测算法在智能交通领域有着重要的应用,但存在小目标检测精度低和多尺度目标信息丢失的问题。针对此问题,提出一种改进的YOLOv3_4d算法,可以实现复杂交通场景中多尺度目标的精准识别。首先,为解决不同尺度目标在常规算法中信息丢失的问题,在检测网络中增加128×128尺度构造多尺度检测网络,挖掘不同尺度信息。其次,设计注意力残差单元和特征增强模块,注意力残差单元提取深层次小目标语义特征信息,特征增强模块拼接多尺度特征,获取丰富的融合信息。最后,算法引入GIoU和Focal函数设计损失函数,加快收敛速率,增强鲁棒性。数据集BDD 100K和VOC 2012的实验表明,改进算法的mAP50值增加了9.2%和4.0%,小目标的mAP值增加了2.3%和3.2%,充分证实本算法的可行性。

Object detection algorithm has an important application in the field of intelligent transportation,but it has two problems:low accuracy of small targets and loss of multi-scale information.To solve these problems,an improved YOLOv3_4 d algorithm is proposed in this paper,which can achieve recognition of multi-scale targets in complex traffic scenes.Firstly,in order to solve the problem of information loss of different targets in the common algorithm,a multi-scale detection network is constructed by adding 128128 scale to the detector to mine information with different scales.Secondly,the attention residual unit and feature enhancement module are designed.The attention residual unit extracts semantic feature information of small targets,and the feature enhancement module splices multi-scale feature maps to obtain rich fusion information.Finally,the GIoU and Focal functions were introduced to accelerate the convergence speed and enhance the robustness of the algorithm.The experimental results of the BDD 100 K and VOC 2012 show that the mAP is improved by 9.2%and 4.0%,and the mAPof small targets is improved by 2.3%and 3.2%,which fully confirms the feasibility of the proposed algorithm.