soft_nms_results=soft_nms(detections,iou_threshold=0.5)print("soft-nms算法结果:")forresultinsoft_nms_results:print("目标框:",result.xmin,result.ymin,result.xmax,result.ymax,"置信度:",result.confidence) 这个示例代码演示了如何创建目标框对象,并使用NMS和soft-nms算法对行人检测结果进行筛选。最终输...
最终输出了经过NMS和soft-nms算法筛选后的结果。 soft-nms算法是一种用于目标检测中非最大值抑制(Non-maximum Suppression,NMS)的改进方法。它的主要目标是解决传统NMS算法在目标重叠较大时可能会删除一些正确的边界框的问题。虽然soft-nms算法在某些情况下可以提供更好的性能,但它仍然存在一些缺点和类似的替代方法。
4. choose the highest score value a_2 left at set B and add a_2 to set C 5. repeat the 2-4 until there is nothing in set B, while set C is the NMS value set """importnumpyasnp# boxes表示人脸框的xywh4点坐标+相关置信度boxes = np.array([[100,100,210,210,0.72], [250,250...
4. choose the highest score value a_2 left at set B and add a_2 to set C 5. repeat the 2-4 until there is nothing in set B, while set C is the NMS value set """importnumpyasnp# boxes表示人脸框的xywh4点坐标+相关置信度boxes = np.array([[100,100,210,210,0.72], [250,250...