Abstract
One-stage object detectors are simple and efficient; however, they cannot extract sufficient object features due to simplistic structures. At the same time, the classification score cannot reflect the actual positioning of the candidate box. Therefore, it is not accurate to use classification score only as the candidate box position score in non-maximum suppression (NMS) stage. These two shortcomings degrade the detection accuracy. In this paper, a novel feature pyramid architecture named refined feature pyramid network (ReFPN) is introduced to obtain better object features. ReFPN designs a refined module which is parallel with feature pyramid network (FPN) to extract the semantic features of objects, and then the extraction of features are used to optimize the features of FPN by summation. In addition, we design the refined center-ness (RCenter-ness) branch that predicts the position score of each point on the feature map to improve the localization accuracy. The predicted position score is multiplied by the classification score to obtain the final position score that has a stronger correlation with localization accuracy. The final position score is inputted to the subsequent NMS, which improves localization accuracy. The proposed method in this paper is named ReFPN-FCOS. The sufficient experiments on COCO2017 datasets demonstrate the effectiveness of ReFPN-FCOS on improving classification accuracy and localization accuracy. The average precisions of this method achieve 1.1% and 1.3 % higher than those of FCOS, when using ResNet50 and ResNet101 as backbone respectively. Code download link: https://github.com/xjl-le/mmdete
Highlights
In recent years, artificial intelligence has been hot all the time
During inference stage, most of the current object detectors still use classification scores as the position scores in the non-maximum suppression (NMS) [23] stage, which degrades the accuracy of the model
The refined feature pyramid network (ReFPN)-FCOS framework is shown in Figure 2, ReFPN designs a refined module based on feature pyramid network (FPN) to fuses the features of multi-layer feature map, this module is in parallel with FPN structure and optimizes the original FPN feature maps
Summary
Artificial intelligence has been hot all the time. Under the support of deep learning theory, object detection is developing rapidly. Multi-stage object detectors use the networks to detect the objects for many times. In this process, a large number of invalid prediction boxes are eliminated, and the prediction boxes of the positive samples are optimized for many times. During inference stage, most of the current object detectors still use classification scores as the position scores in the non-maximum suppression (NMS) [23] stage, which degrades the accuracy of the model To solve this problem, this paper designs the refined center-ness (RCenter-ness) branch, which takes the object context information as input to predict the position score of the prediction box. The average accuracy on ResNet101 is 41.8%, which is higher than 40.5% of the original FCOS
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