A Small Object Detection Network Based on Multiple Feature Enhancement and Feature Fusion

Abstract

Due to the small size, high resolution, and complex background, small object detection has become a difficult point in computer vision. Making full use of high-resolution features and reducing information loss in the process of information propagation is of great significance to improve small object detection. In this article, to achieve the above two points, this work proposes a small object detection network based on multiple feature enhancement and feature fusion based on RetinaNet (MFEFNet). First, this work designs a densely connected dilated convolutions to adequately extract high-resolution features from C2. Then, this work utilizes subpixel convolution to avoid the loss of channel information caused by channel dimension reduction in the lateral connection. Finally, this article introduces a bidirectional fusion feature pyramid structure to shorten the propagation path of high-resolution features and reduce the loss of high-resolution features. Experiments show that our proposed MFEFNet achieves stable performance gains in object detection task. Specifically, the improved method improves RetinaNet from 34.4AP to 36.2AP on the challenging MS COCO dataset, and especially achieves excellent results in small object detection with an improvement of 2.9%.

Fabric defect detection based on feature enhancement and complementary neighboring information

“…This method has a significant effect on improving feature selectivity, but it incurs considerable computational overhead when processing largescale images. Tan et al [27] designed a sub-pixel convolutional enhancement module, which utilizes sub-pixel convolution to convert low-resolution images into high-resolution ones, thereby reducing information loss caused by channel reduction and preserving more spatial information of features. This method effectively retains spatial details of features but faces computational challenges when processing highresolution images.…”