BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection

BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection

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Small object detection aluminum lotion becomes a challenging problem in computer vision due to low resolution and less feature information.Making full use of high-resolution features is an important factor in improving small object detection.In this paper, to improve the utilization of high-resolution features, this work proposes the Bidirectional Multi-scale Feature Enhancement Network (BFE-Net) based on RetinaNet.First, this work introduces a bidirectional feature pyramid structure to shorten the propagation path of high-resolution features.

Then, this work utilizes residually connected dilated convolutional blocks to fully extract high-resolution features of low-feature layers.Finally, this work supplements the high-resolution features lost in the high-level feature propagation process by leveraging the high-level guided lower-level features.Experiments show that our weleda skin food 75ml best price proposed BFE-Net achieves stable performance gains in the object detection task.Specifically, the improved method improves RetinaNet from 34.

4 AP to 36.3 AP on the challenging MS COCO dataset and especially achieves excellent results in small object detection with an improvement of 2.8%.