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%.

中文翻译：

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一种基于多重特征增强和特征融合的小目标检测网络

由于体积小、分辨率高、背景复杂等特点，小物体检测成为计算机视觉中的难点。充分利用高分辨率特征，减少信息传播过程中的信息丢失，对于提高小目标检测具有重要意义。在本文中，为了实现以上两点，本工作提出了一种基于RetinaNet（MFEFNet）的基于多重特征增强和特征融合的小物体检测网络。首先，这项工作设计了一个密集连接的空洞卷积来充分地从C ₂中提取高分辨率特征. 然后，这项工作利用亚像素卷积来避免横向连接中通道降维导致的通道信息丢失。最后，本文引入双向融合特征金字塔结构，缩短高分辨率特征的传播路径，减少高分辨率特征的损失。实验表明，我们提出的 MFEFNet 在目标检测任务中实现了稳定的性能提升。具体来说，改进后的方法在具有挑战性的 MS COCO 数据集上将 RetinaNet 从 34.4AP 提高到 36.2AP，尤其是在小物体检测方面取得了优异的成绩，提高了 2.9%。