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Conference Paper Performance Enhancement of YOLOv3 by Adding Prediction Layers with Spatial Pyramid Pooling for Vehicle Detection
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Authors
Kwang-Ju Kim, Pyong-Kun Kim, Yun-Su Chung, Doo-Hyun Choi
Issue Date
2018-11
Citation
International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2018, pp.411-416
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/AVSS.2018.8639438
Project Code
18ZD1100, Development of ICT Convergence Technology for Daegu-GyeongBuk Regional Industry, Moon Ki Young
Abstract
In recent years, vision-based object detection methods using convolutional neural network (CNN) have been very successful. However, the object detection method using the CNN feature has a disadvantage that lots of feature maps should be generated in order to be robust against the scale change and the occlusion of the object. Also, simply raising a large number of feature maps does not improve performance. We propose a multi-scale vehicle detection with spatial pyramid pooling method which is robust to the scale change of the vehicle and the occlusion by improving the conventional YOLOv3 algorithm. The proposed method was evaluated through the UA-DETRAC benchmark and obtain the state-of-the-art mAP, which is better than those of the DPM, ACF, R-CNN, CompACT, NANO, SA-FRCNN, and Faster-RCNN2.
KSP Keywords
CNN features, Convolution neural network(CNN), Detection Method, Feature Map, Multi-scale, Pooling method, R-CNN, Spatial Pyramid Pooling, Vehicle detection, performance enhancement, state-of-The-Art