Abstract
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.
Highlights
Object detection is a fundamental, and practical, research branch in the field of computer vision, practicing border and category prediction of each instance object in an image by corresponding algorithms
To address the aforementioned target detection problems, this study proposes an object-detection method based on center point proposals by integrating the advantages of FCOS and OaP (As shown in Figure 2b, OaP algorithm could predict the center point on the heatmap, facilitating positioning and detection of the cell)
center point proposals (CPO), YOLOv3 and FCOS, the bounding boxes output by OaP appears slightly offset, because its center points have a strong correlation with the two freedom variables that affect prediction results, while the predictors of the remaining detectors are independent, leading to better bounding box regression
Summary
Object detection is a fundamental, and practical, research branch in the field of computer vision, practicing border and category prediction of each instance object in an image by corresponding algorithms. One-stage detectors would score the anchor boxes distributed densely on the image and generate the final bounding box prediction by improving their coordinates through regression. These algorithms have proved successful, the following problems are still noteworthy: 1. Shot Detector) [9], 100 K in RetinaNet [7], and 180 K in feature pyramid networks (FPN) [11] for images with the short side length of 800 Most of these anchor boxes would be labeled as negative samples during training, the excessive negative samples may aggravate the imbalance between positive and negative samples in training.
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