Method

IoU-Adaptive Deformable Cascade R-CNN [IoU_DCRCNN]
[Anonymous Submission]

Submitted on 2 Jan. 2019 13:58 by
[Anonymous Submission]

Running time:0.66 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
we propose IoU-guided cascading detection framework to reduce the loss of small object information during training. Besides, the IoU-based weighted loss is designed, which can learn the IoU information of positive ROIs to improve the detection accuracy effectively.
Parameters:
IoU threshold for cascade detectors are 0.5,0.6,0.7
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) 87.68 % 84.48 % 76.70 %
Car (Orientation) 38.00 % 33.60 % 31.21 %
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2D object detection results.
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Orientation estimation results.
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