Integrally Mixing Pyramid Representations for Anchor-Free Object Detection in Aerial Imagery
Abstract: Anchor-free object detectors have recently received increasing research attention in the field of aerial scene object detection, due to their high flexibility and practicality. Anchor-free detectors typically depend on the feature pyramid network (FPN) to alleviate the challenge of significant variations in object scales in aerial contexts. Despite establishing a multiscale feature pyramid, existing FPN-based methods treat each aerial object as an indivisible entity solely managed by a single-scale representation. However, they fail to take into account the distinct characteristics of various components within an instance. To this end, this letter proposes a novel anchor-free detector, namely IMPR-Det, which can integrally mix multiscale pyramid representations for different components of an instance, thus boosting the fine-grained object representation capability. Specifically, IMPR-Det fundamentally introduces a more advanced detection head with an adaptive routing mechanism for pixel-level multiscale feature assignment, instead of previous instance-level assignment. Experimental results demonstrate the superiority of the proposed method over its counterparts, in terms of both accuracy and efficiency, for object detection in aerial images.
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