AARN: Anchor‐guided attention refinement network for inshore ship detection
Di Liu, Yan Zhang, Yan Zhang, Yan Zhao, Zhiguang Shi, Jinghua Zhang, Yu Zhang, Yu Zhang, Feng Ling, Yi Zhang, Yi Zhang
Abstract
Abstract Inshore ship detection is an important task in several fields, for example, maritime transportation, maritime supervision, and port management. However, due to the diversified categories and locations of different ships and interference of complex surroundings, capturing discriminative characteristics of multi‐scale inshore ships for accurate detection is still challenging. Here, an anchor‐guided attention refinement network (AARN) is proposed to alleviate the problems by prominently designing an attention feature filter module (AFFM) and an anchor‐guided alignment detection module (AADM). In AFFM, of which the attention supervision generated from high‐level semantic features, is used to highlight informative target cues and suppress background interference when establishing a four‐layer feature pyramid. In AADM, the anchor‐aligned features are adopted to eventually identify potential inshore ships, which both alleviates misalignment between refined anchors and pyramidal features and improves the performance further. Extensive experiments conducted on the public Seaships7000 dataset verify the contributions of the proposed modules and the effectiveness of our method for detecting multi‐scale inshore ships in comparison to both domain‐specific and general CNN‐based methods.