Research on Pre-labelling Algorithm for Multi-model Fusion Voting
Aiming at the two problems of cumbersome and time-consuming annotation content,a pre-labelling algorithm for multi-model fusion voting is proposed.In the pre-labelling process,the detection results of the three models of Cascade_RCNN,RetinaNet and CondLaneNet are fused,and then the coordinate results generated by each model are processed by extracting,judging,matching,averaging of parameters,sorting and so on,to obtain the final pre-labelling results.The results of multiple tests on the public datasets and the self-constructed datasets show that the algorithm is able to improve the accuracy of pre-labelling and reduce the manual labelling workload in the process of labelling,which has a better effect and verifies the effectiveness of the method.
Deep Learningtarget detectionlaneline detectionpre-labellingmodel fusion
国家科技期刊平台
NSTL
万方数据
