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Ship detection in aerial images remains an active yet challenging task due toarbitrary object orientation and complex background from a bird's-eyeperspective. Most of the existing methods rely on angular prediction orpredefined anchor boxes, making these methods highly sensitive to unstableangular regression and excessive hyper-parameter setting. To address theseissues, we replace the angular-based object encoding with ananchor-and-angle-free paradigm, and propose a novel detector deploying a centerand four midpoints for encoding each oriented object, namely MidNet. MidNetdesigns a symmetrical deformable convolution customized for enhancing themidpoints of ships, then the center and midpoints for an identical ship areadaptively matched by predicting corresponding centripetal shift and matchingradius. Finally, a concise analytical geometry algorithm is proposed to refinethe centers and midpoints step-wisely for building precise oriented boundingboxes. On two public ship detection datasets, HRSC2016 and FGSD2021, MidNetoutperforms the state-of-the-art detectors by achieving APs of 90.52% and86.50%. Additionally, MidNet obtains competitive results in the ship detectionof DOTA.

MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images