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AggNet: Simple Aggregated Network for Real-Time Multiple Object Detection in Road Driving Scene

Title
AggNet: Simple Aggregated Network for Real-Time Multiple Object Detection in Road Driving Scene
Authors
Won, Woong JaeKim, Tae HunChoi, Min-KookKwon, Soon
DGIST Authors
Choi, Min-Kook; Kwon, Soon
Issue Date
2018-11-07
Citation
21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018, 3505-3510
Type
Conference
ISBN
9781728103235
Abstract
A real-time object detection model in road driving scene is being established as an important component technology that facilitates autonomous driving; its performance has been improving drastically due to progress in deep learning technology. However, the performances for occlusion between objects and small object detection are not yet perfect. In this paper, a simple aggregated convolution neural network (AggNet) based real-time multiple object detection model is proposed to improve the detection performance of occlusion or small objects in road driving scene. The proposed model is designed to deliver feature information of a small receptive field by improving the residual block of the traditional Residual Network (ResNet) with the simple aggregation block. Furthermore, to detect objects of various sizes effectively, an aggregated rezoom layer-based object detection method was applied instead of the conventional multi-scale feature and anchor-based object detection method. As a result of tests using KITTI data, it was confirmed that the proposed model can successfully detect small objects and object occlusions of various sizes. 1024x320 input images can be processed at 20-22 fps with an NVIDIA Titan XP GPU. © 2018 IEEE.
URI
http://hdl.handle.net/20.500.11750/9554
DOI
10.1109/ITSC.2018.8569393
Publisher
IEEE Intelligent Transportation Systems Society
Related Researcher
  • Author Kwon, Soon  
  • Research Interests computer vision; deep learning; autonomous driving; parallel processing; vision system on chip
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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