Slender Object Detection: Diagnoses and Improvements
This work tackles a specific problem of detecting slender objects, which are crucial in many real-world scenarios, for object detection systems. It is an incremental improvement to existing methods.
This paper addresses the problem of detecting slender objects, which are common but often overlooked in object detection. The authors found that existing methods show a drastic drop of 18.9% mAP on COCO when evaluated solely on slender objects. They propose a feature adaptation strategy that improves detection of these objects.
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.