Version 1
: Received: 28 July 2023 / Approved: 31 July 2023 / Online: 31 July 2023 (10:40:08 CEST)
How to cite:
Zhu, C.; Liang, J.; Zhou, F. Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection. Preprints2023, 2023072106. https://doi.org/10.20944/preprints202307.2106.v1
Zhu, C.; Liang, J.; Zhou, F. Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection. Preprints 2023, 2023072106. https://doi.org/10.20944/preprints202307.2106.v1
Zhu, C.; Liang, J.; Zhou, F. Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection. Preprints2023, 2023072106. https://doi.org/10.20944/preprints202307.2106.v1
APA Style
Zhu, C., Liang, J., & Zhou, F. (2023). Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection. Preprints. https://doi.org/10.20944/preprints202307.2106.v1
Chicago/Turabian Style
Zhu, C., Jiarui Liang and Fei Zhou. 2023 "Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection" Preprints. https://doi.org/10.20944/preprints202307.2106.v1
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 YOLOv3 approach for identifying dense objects in the road has been proposed. Firstly, 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 reduced size of the network model can further reduce the training and detection time, and can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the traditional YOLOv2 by 3.05% and 11.15%, respectively. Besides, the test was carried out using UA-DETRAC, a public road vehicle detection dataset, the object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the speed is 4.3 Fps/s faster; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3 and the newer object detection algorithm YOLOv5.
Keywords
dense road; object detection; Darknet-53 network; transfer learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
