Publication | Closed Access
Refining faster-RCNN for accurate object detection
69
Citations
9
References
2017
Year
Unknown Venue
Image ClassificationConvolutional Neural NetworkMachine VisionImage AnalysisMachine LearningObject DetectorPattern RecognitionObject DetectionObject RecognitionMedical Image ComputingAccurate Object DetectionRefining BlockRegion Proposal NetworksComputer ScienceEngineeringDeep LearningVision RecognitionComputer Vision
Object detector with region proposal networks such as Fast/Faster R-CNN [1, 2] have shown the state-of-the art performance on several benchmarks. However, they have limited success for detecting small objects. We argue the limitation is related to insufficient performance of Fast R-CNN block in Faster R-CNN. In this paper, we propose a refining block for Fast R-CNN. We further merge the block and Faster R-CNN into a single network (RF-RCNN). The RF-RCNN was applied on plate and human detection in RoadView image that consists of high resolution street images (over 30M pixels). As a result, the RF-RCNN showed great improvement over the Faster-RCNN.
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