基于深度重塑的航拍目标检测增强网络

Depth-Reshaping Based Aerial Object Detection Enhanced Network

针对航拍图像目标检测中存在的复杂背景对检测的干扰、小目标的细节丢失及检测效率的高需求等问题,文中提出深度重塑增强网络(Depth-Reshaping Enhanced Network,DR-ENet).首先,采用空间深度重塑技术取代传统下采样方法,减少特征提取中的信息损失,增强对细节的捕获能力.然后,提出可变形空间金字塔池化方法,增强网络对目标形状变化的适应性和在复杂背景中目标识别的能力.同时,注意力解耦检测头增强针对各检测任务的学习效果.最后,为了同时兼顾密集小目标和复杂背景的特点,构建小型航拍数据集PORT.在3个公开航拍数据集及PORT数据集上的测试表明DR-ENet有一定的性能提升,说明其在航拍图像目标检测中的有效性和高效性.

To address the issues of complex background interference,loss of fine details in small objects and the high demand for detection efficiency in aerial image object detection,a depth-reshaping enhanced network(DR-ENet)is proposed.Firstly,the traditional downsampling methods are replaced by spatial depth-reshaping techniques to reduce information loss during feature extraction and enhance the ability of the network to capture details.Then,a deformable spatial pyramid pooling method is designed to enhance the adaptability of network to object shape variations and its ability to recognize in complex backgrounds.Simultaneously,an attention decoupling detection head is proposed to enhance the learning effectiveness for different detection tasks.Finally,a small-scale aerial dataset,PORT,is constructed to simultaneously consider the characteristics of dense small objects and complex backgrounds.Experiments on three public aerial datasets and PORT dataset demonstrate that DR-ENet achieves performance improvement,proving its effectiveness and high efficiency in aerial image object detection.