Method

ResNet18-based Recurrent Rolling Convolution (Advanced Hardware, with noise) [ResNet-RRC (Noised)]
[Anonymous Submission]

Submitted on 10 Aug. 2018 08:50 by
[Anonymous Submission]

Running time:.057 s
Environment:GPU @ 1.5 Ghz (Python + C/C++)

Method Description:
This is the Resnet-RRC trained and tested on noised
images.
Parameters:
Same as Resnet-RRC trained on original KITTI images
Latex Bibtex:

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 78.97 % 71.81 % 63.57 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot




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