Detection accuracy and speed are crucial in object detection in computer vision. This work proposes a novel technique called On-Category Anchors (OC-Anchors) to improve the accuracy of real-time single-stage object detectors. The key concept of the OC-Anchors technique is to create anchors based on the categories of foreground objects. The OC-Anchors are set to reflect the bounding box features of the foreground object category. This approach improves the accuracy of predicting the bounding boxes of objects. The performance of the proposed OC-Anchors technique is examined in detail in the YOLOv2 framework with the COCO dataset. The results show that the OC-Anchors technique significantly improves the detection accuracy in tests on COCO test-dev, without substantially affecting the prediction speed. The improvement in average precision ranges from 21.6 to 27.1%.

检测精度和速度在计算机视觉中的目标检测中至关重要。这项工作提出了一种称为 On-Category Anchors (OC-Anchors) 的新技术，以提高实时单级目标检测器的准确性。OC-Anchors 技术的关键概念是根据前景对象的类别创建锚点。OC-Anchors 设置为反映前景对象类别的边界框特征。这种方法提高了预测对象边界框的准确性。使用 COCO 数据集在 YOLOv2 框架中详细检查了所提出的 OC-Anchors 技术的性能。结果表明，OC-Anchors 技术在 COCO test-dev 上的测试中显着提高了检测精度，而不会显着影响预测速度。