(Retracted) Transfer learning-based YOLOv3 model for road dense object detection

Abstract

Stemming from the object overlap and undertraining from the few samples, the road dense object detection is confronted with the poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based you only look once, version 3 (YOLOv3) approach for identifying dense objects in the road has been proposed. First, Darknet-53 network structure is adopted to obtain pre-trained YOLOv3 model, then the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles; in the proposed model, one random function is adapted to intialize and optimize the weights of the transfer training model, which is seperately designed from the pre-trained YOLOv3; and the object detection classifier replaces the fully connected layer, which further improves the detection effect. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal Visual Object Classes (VOC) 2007 dataset, which is superior to the YOLOv2 and the traditional R-CNN by 11.05% and 0.8%, respectively. In addition, the detection speed of the proposed YOLOv3 method reaches 27.3 frames per second (Fps)/s in detecting images, which is 6.4 Fps / s faster than the traditional YOLOv3; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3.