Anchor-based detectors are widely adopted in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet most of them are invalid. Although anchor-free methods can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes an object-detection method based on center point proposals to reduce the number of useless anchor boxes while improving the quality of anchor boxes, balancing the proportion of positive and negative samples. By introducing the differentiation module in the shallow layer, the new method can alleviate the problem of missing detection caused by overlapping of center points. When trained and tested on COCO (Common Objects in Context) dataset, this algorithm records an increase of about 2% in APS
(Average Precision of Small Object), reaching 27.8%. The detector designed in this study outperforms most of the state-of-the-art real-time detectors in speed and accuracy trade-off, achieving the AP of 43.2 in 137 ms.
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