IoU-aware Single-stage Object Detector for Accurate Localization
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Due to the simpleness and high efficiency, single-stage object detectors have been widely applied in many computer vision applications . However, the low correlation between the classification score and localization accuracy of the predicted detections has severely hurt the localization accuracy of models. In this paper, IoU-aware single-stage object detector is proposed to solve this problem. Specifically, IoU-aware single-stage object detector predicts the IoU between the regressed box and the ground truth box. Then the classification score and predicted IoU are multiplied to compute the detection confidence, which is highly correlated with the localization accuracy. The detection confidence is then used as the input of NMS and COCO AP computation, which will substantially improve the localization accuracy of models. Sufficient experiments on COCO and PASCAL VOC dataset demonstrate the effectiveness of IoU-aware single-stage object detector on improving the localization accuracy. Without whistles and bells, the proposed method can substantially improve AP by 1.0%∼1.6% on COCO test-dev and 1.1%∼2.2% on PASCAL VOC2007 test compared with the baseline. The improvement for AP at higher IoU threshold(0.7∼0.9) is 1.7%∼2.3% on COCO test-dev and 1.0%∼4.2% PASCAL VOC2007 test. The source code will be made publicly available.
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