In general object detection, scale variation is always a big challenge. At present, feature pyramid networks are employed in numerous methods to alleviate the problems caused by large range of objects which makes use multi-level features extracted from backbone for top-down upsampling and fusion acquire set multi-scale depth image features. However, network proposed Ghiasi et al. adopts simple ...

Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) or eight four vertices) to describe bounding box l1 loss as function. In this paper, we argue that aforementioned integration can cause training instability performance degeneration. The main reason is discontinuity which caused by contradiction between definition ...

Aiming at the problems of low detection accuracy for objects with non-significant features in FCOS network, a new object method based on attention mechanism is proposed to improve performance which can effectively guide network focus detailed features. According verification experimental results KITTI dataset, AP value improved mechanism-based car and person by 1.1% 4.9% compared standard respe...

A wide variety of methods have been developed to approach the problem of salient object detection. The performance of these methods is often image-dependent. This paper aims to develop a method that is able to select for an input image the best salient object detection result from many results produced by different methods. This is a challenging task as different salient object detection result...