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Open AccessArticle

Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights

by Fan Yang 1,2,*, Deming Yang 1,*, Zhiming He 1, Yuanhua Fu 1 and Kui Jiang 1
1
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
*
Authors to whom correspondence should be addressed.
Algorithms 2020, 13(5), 114; https://doi.org/10.3390/a13050114
Received: 17 March 2020 / Revised: 28 April 2020 / Accepted: 29 April 2020 / Published: 2 May 2020
(This article belongs to the Special Issue Algorithms for Smart Cities)
Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification accuracy of distant cars, we propose a reformed YOLOv3 (You Only Look Once, version 3) algorithm to realize the detection of various types of automobiles, such as SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks and pickup trucks. Based on the dataset UA-DETRAC-LITE, manually labeled data is added to improve the data balance. First, data optimization for the vehicle target is performed to improve the generalization ability and position regression loss function of the model. The experimental results show that, within the range of 67 m, and through scale optimization (i.e., by introducing multi-scale training and anchor clustering), the classification accuracies of trucks and pickup trucks are raised by 26.98% and 16.54%, respectively, and the overall accuracy is increased by 8%. Secondly, label smoothing and mixup optimization is also performed to improve the generalization ability of the model. Compared with the original YOLO algorithm, the accuracy of the proposed algorithm is improved by 16.01%. By combining the optimization of the position regression loss function of GIOU (Generalized Intersection Over Union), the overall system accuracy can reach 92.7%, which improves the performance by 21.28% compared with the original YOLOv3 algorithm. View Full-Text
Keywords: smart streetlight; YOLOv3; multi-scale training; anchor clustering; label smoothing; mixup; IOU; GIOU; fine-grained classification of automobile smart streetlight; YOLOv3; multi-scale training; anchor clustering; label smoothing; mixup; IOU; GIOU; fine-grained classification of automobile
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Yang, F.; Yang, D.; He, Z.; Fu, Y.; Jiang, K. Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights. Algorithms 2020, 13, 114.

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