  In recent years, the use of object proposal as a preprocessing step for
target detection to improve computational efficiency has become an effective
method. Good object proposal methods should have high object detection recall
rate and low computational cost, as well as good localization quality and
repeatability. However, it is difficult for current advanced algorithms to
achieve a good balance in the above performance. For this problem, we propose a
class-independent object proposal algorithm BIHL. It combines the advantages of
window scoring and superpixel merging, which not only improves the localization
quality but also speeds up the computational efficiency. The experimental
results on the VOC2007 data set show that when the IOU is 0.5 and 10,000 budget
proposals, our method can achieve the highest detection recall and an mean
average best overlap of 79.5%, and the computational efficiency is nearly three
times faster than the current fastest method. Moreover, our method is the
method with the highest average repeatability among the methods that achieve
good repeatability to various disturbances.
