论文标题
量子柔软的QUBO抑制以进行准确的对象检测
Quantum-soft QUBO Suppression for Accurate Object Detection
论文作者
论文摘要
数十年来，已默认采用了非最大最大抑制（NMS）来删除冗余对象检测。它仅通过保持具有最高检测得分的图像M和与M的重叠比小于预定义阈值的图像来消除误报。但是，这种贪婪的算法在遮挡场景下可能无法很好地用于对象检测，在遮挡场景中，可能会抑制较低检测分数的真实阳性。在本文中，我们首先将删除冗余检测的任务映射到二进制二进制优化（QUBO）框架中，该框架由每个边界框中的检测得分组成，并在一组边界框之间进行重叠比。接下来，我们使用所提出的量子 - 软QUBO抑制（QSQS）算法解决QUBO问题，以通过利用量子计算优势来快速准确地检测。实验表明，QSQS的平均精度从Pascal VOC 2007的平均精度从74.20％提高到75.11％。对于合理的基准测试人行人检测城市，它始终优于NMS和SOFT-NMS。
Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. It eliminates false positives by only keeping the image M with highest detection score and images whose overlap ratio with M is less than a predefined threshold. However, this greedy algorithm may not work well for object detection under occlusion scenario where true positives with lower detection scores are possibly suppressed. In this paper, we first map the task of removing redundant detections into Quadratic Unconstrained Binary Optimization (QUBO) framework that consists of detection score from each bounding box and overlap ratio between pair of bounding boxes. Next, we solve the QUBO problem using the proposed Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages. Experiments indicate that QSQS improves mean average precision from 74.20% to 75.11% for PASCAL VOC 2007. It consistently outperforms NMS and soft-NMS for Reasonable subset of benchmark pedestrian detection CityPersons.
