A fully convolutional network regresses objectness from sonar images to achieve 96% recall using only 100 proposals per image, outperforming EdgeBoxes and Selective Search in efficiency.
How good are detection proposals, really?
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abstract
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Learning Objectness from Sonar Images for Class-Independent Object Detection
A fully convolutional network regresses objectness from sonar images to achieve 96% recall using only 100 proposals per image, outperforming EdgeBoxes and Selective Search in efficiency.