摘要
Abstract
Although the current testing technology is becoming increasingly mature,the detection of small targets in complex environments is still the most difficult point in research.Aiming at the problem of high target proportion of traffic signs in road traffic scenarios,the problem of high target proportion of small targets and large environmental inter-ference factors,it proposes a type of road traffic logo target test algorithm based on YOLOv8 improvement.Due to the prone to missed inspection in small target testing,the bi-level routing attention(BRA)attention mechanism is used to improve the network's perception of small targets.In addition,it also uses a shape-changing convolutional module deformable convolution V3(DCNV3).It has a better feature extraction ability for irregular shapes in the feature map,so that the backbone network can better adapt to irregular space structures,and pay more accurately to important attention,objectives,thereby improving the detection ability of the model to block the overlapping target.Both DCNV3 and BRA modules improve the accuracy of the model without increasing the weight of the model.At the same time,the Inner-IOU loss function based on auxiliary border is introduced.On the four data sets of RoadSign,CCTSDB,TSDD,and GTSDB,small sample training,large sample training,single target detection,and multi-target detection are performed.The experi-mental results are improved.Among them,the experiments on the RoadSign data set are the best.The average accuracy of the improved YOLOv8 model mAP50 and mAP50:95 reaches 90.7%and 75.1%,respectively.Compared with the baseline model,mAP50 and mAP50:95 have increased by 5.9 and 4.8 percentage points,respectively.The experimental results show that the improved YOLOV8 model effectively implements the traffic symbol detection in complex road scenarios.关键词
YOLOv8/小目标检测/可形变卷积/注意力机制/复杂道路场景Key words
YOLOv8/small target detection/deformable convolution/attention mechanism/complex road scenes分类
信息技术与安全科学
