Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data

The recent research by deep learning has shown many breakthroughs with high performance that were not achieved with traditional machine learning algorithms. Particularly in the field of object detection, commercial products with high accuracy in the real environment are applied through the deep lear...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.78311-78319
Main Authors: Kim, Kwang-Ju, Kim, Pyong-Kun, Chung, Yun-Su, Choi, Doo-Hyun
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolution
Deep learning
Detectors
Feature extraction
Feature maps
Machine learning
neural networks
Object detection
Object recognition
Occlusion
Performance enhancement
Proposals
scale variation
Surveillance
Traffic surveillance
UA-DETRAC benchmark
yolo
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Summary:The recent research by deep learning has shown many breakthroughs with high performance that were not achieved with traditional machine learning algorithms. Particularly in the field of object detection, commercial products with high accuracy in the real environment are applied through the deep learning methods. However, the object detection method using the convolutional neural network (CNN) has a disadvantage that a large number of feature maps should be generated in order to be robust against scale change and occlusion of the object. Also, simply raising the number of feature maps does not improve performance. In this paper, we propose to integrate additional prediction layers into conventional Yolo-v3 using spatial pyramid pooling to complement the detection accuracy of the vehicle for large scale changes or being occluded by other objects. Our proposed detector achieves 85.29% mAP, which outperformed than those of the DPM, ACF, R-CNN, CompACT, NANO, EB, GP-FRCNN, SA-FRCNN, Faster-R CNN2, HAVD, and SSD-VDIG on the UA-DETRAC benchmark data-set consisting of challenging real-world-traffic videos.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2922479