  In this paper, we are concerned with the detection of a particular type of
objects with extreme aspect ratios, namely \textbf{slender objects}. In
real-world scenarios, slender objects are actually very common and crucial to
the objective of a detection system. However, this type of objects has been
largely overlooked by previous object detection algorithms. Upon our
investigation, for a classical object detection method, a drastic drop of
$18.9\%$ mAP on COCO is observed, if solely evaluated on slender objects.
Therefore, we systematically study the problem of slender object detection in
this work. Accordingly, an analytical framework with carefully designed
benchmark and evaluation protocols is established, in which different
algorithms and modules can be inspected and compared. \New Our study reveals
that effective slender object detection can be achieved ~\textbf{with none of}
(1) anchor-based localization; (2) specially designed box representations.
Instead, \textbf{the critical aspect of improving slender object detection is
feature adaptation}. It identifies and extends the insights of existing methods
that are previously underexploited. Furthermore, we propose a feature adaption
strategy that achieves clear and consistent improvements over current
representative object detection methods.
