The Improved CenterNet for Ship Detection in Scale-Varying Images

Xujia Hou, Feihu Zhang
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Abstract

Recently, deep learning for object detection has been widely used in face recognition, traffic detection, and other fields. However, due to the dataset limitation and the target scale variation issues, ship detection is not as perfect as in other fields. To address such issues, in this paper, an anchor-free detection method is proposed in framework of CenterNet. By improving the network structure and using the unique activation function, the detection issues are successfully solved. Experimental results show that the improved CenterNet method is 5.6% higher in mAP in contrast to the state-of-the-art, and the FPS is increased by 4, respectively.
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基于改进中心网的变尺度图像船舶检测
近年来,深度学习在物体检测中的应用已广泛应用于人脸识别、交通检测等领域。然而,由于数据集的限制和目标尺度的变化问题,船舶检测并不像其他领域那样完善。针对这些问题,本文提出了一种基于CenterNet框架的无锚检测方法。通过改进网络结构和利用独特的激活函数,成功地解决了检测问题。实验结果表明,改进后的CenterNet方法的mAP和FPS分别比现有方法提高了5.6%和4%。
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