  The localization of fruits is an essential first step in automated
agricultural pipelines for yield estimation or fruit picking. One example of
this is the localization of apples in images of entire apple trees. Since the
apples are very small objects in such scenarios, we tackle this problem by
adapting the object proposal generation system AttentionMask that focuses on
small objects. We adapt AttentionMask by either adding a new module for very
small apples or integrating it into a tiling framework. Both approaches clearly
outperform standard object proposal generation systems on the MinneApple
dataset covering complex apple orchard environments. Our evaluation further
analyses the improvement w.r.t. the apple sizes and shows the different
characteristics of our two approaches.
