  We propose two improvements to the SSD---single shot multibox detector.
First, we propose an adaptive approach for default box selection in SSD. This
uses data to reduce the uncertainty in the selection of best aspect ratios for
the default boxes and improves performance of SSD for datasets containing small
and complex objects (e.g., equipments at construction sites). We do so by
finding the distribution of aspect ratios of the given training dataset, and
then choosing representative values. Secondly, we propose an ensemble
algorithm, using SSD as components, which improves the performance of SSD,
especially for small amount of training datasets. Compared to the conventional
SSD algorithm, adaptive box selection improves mean average precision by 3%,
while ensemble-based SSD improves it by 8%.
