Single-stage object detectors have been widely applied in many computer vision applications due to their simpleness and high efficiency. However, the low correlation between the classification score and localization accuracy in detection results severely hurts the average precision of the detection model. To solve this problem, an IoU-aware single-stage object detector is proposed in this paper. Specifically, IoU-aware single-stage object detector predicts the IoU for each detected box. Then the predicted IoU is multiplied by the classification score to compute the final detection confidence, which is more correlated with the localization accuracy. The detection confidence is then used as the input of the subsequent NMS and COCO AP computation, which substantially improves the localization accuracy of model. Sufficient experiments on COCO and PASCOL VOC datasets demonstrate the effectiveness of IoU-aware single-stage object detector on improving model's localization accuracy. Without whistles and bells, the proposed method can substantially improve AP by 1.7%–1.9% and AP75 by 2.2%–2.5% on COCO test-dev. And it can also substantially improve AP by 2.9%–4.4% and AP80, AP90 by 4.6%–10.2% on PASCAL VOC. The source code will be made publicly available.

单级物体检测器由于其简单性和高效率而被广泛应用于许多计算机视觉应用中。然而，分类分数与检测结果定位精度之间的低相关性严重损害了检测模型的平均精度。为了解决这个问题，本文提出了一种具有IoU意识的单级目标检测器。具体来说，可识别IoU的单级目标检测器会为每个检测到的盒子预测IoU。然后，将预测的IoU乘以分类分数，以计算最终的检测置信度，该置信度与定位精度更加相关。然后，将检测置信度用作后续NMS和COCO AP计算的输入，从而大大提高了模型的定位精度。在COCO和PASCOL VOC数据集上进行的充分实验证明了支持IoU的单级目标检测器在提高模型定位精度方面的有效性。在没有哨音的情况下，所建议的方法可以使COCO的AP显着提高1.7％–1.9％，AP75显着提高2.2％–2.5％测试开发。在PASCAL VOC上，它还能显着提高AP的2.9％–4.4％和AP80，AP90的4.6％–10.2％。源代码将公开提供。