TY - JOUR
T1 - Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4
AU - Zhao, Jingyi
AU - Hao, Shengnan
AU - Dai, Chenxu
AU - Zhang, Haiyang
AU - Zhao, Li
AU - Ji, Zhanlin
AU - Ganchev, Ivan
N1 - Publisher Copyright:
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
PY - 2022
Y1 - 2022
N2 - Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of images through both channel dimension and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.
AB - Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of images through both channel dimension and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.
KW - Computational modeling
KW - Convolutional neural networks
KW - Data models
KW - Feature extraction
KW - Object detection
KW - Three-dimensional displays
KW - Vehicle detection
UR - http://www.scopus.com/inward/record.url?scp=85123273696&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3143365
DO - 10.1109/ACCESS.2022.3143365
M3 - Article
AN - SCOPUS:85123273696
SN - 2169-3536
VL - 10
SP - 8590
EP - 8603
JO - IEEE Access
JF - IEEE Access
ER -