计算机科学
计算机视觉
人工智能
目标检测
规范化(社会学)
卫星
卫星图像
探测器
遥感
模式识别(心理学)
地质学
人类学
电信
工程类
社会学
航空航天工程
作者
Hang Gong,Tingkui Mu,Qiuxia Li,Haishan Dai,Chunlai Li,Zhiping He,Wenjing Wang,Feng Han,Abudusalamu Tuniyazi,Haoyang Li,Xuechan Lang,Zhiyuan Li,Bin Wang
出处
期刊:Remote Sensing
[MDPI AG]
日期:2022-06-15
卷期号:14 (12): 2861-2861
被引量:6
摘要
Object detection has made tremendous progress in natural images over the last decade. However, the results are hardly satisfactory when the natural image object detection algorithm is directly applied to satellite images. This is due to the intrinsic differences in the scale and orientation of objects generated by the bird’s-eye perspective of satellite photographs. Moreover, the background of satellite images is complex and the object area is small; as a result, small objects tend to be missing due to the challenge of feature extraction. Dense objects overlap and occlusion also affects the detection performance. Although the self-attention mechanism was introduced to detect small objects, the computational complexity increased with the image’s resolution. We modified the general one-stage detector YOLOv5 to adapt the satellite images to resolve the above problems. First, new feature fusion layers and a prediction head are added from the shallow layer for small object detection for the first time because it can maximally preserve the feature information. Second, the original convolutional prediction heads are replaced with Swin Transformer Prediction Heads (SPHs) for the first time. SPH represents an advanced self-attention mechanism whose shifted window design can reduce the computational complexity to linearity. Finally, Normalization-based Attention Modules (NAMs) are integrated into YOLOv5 to improve attention performance in a normalized way. The improved YOLOv5 is termed SPH-YOLOv5. It is evaluated on the NWPU-VHR10 dataset and DOTA dataset, which are widely used for satellite image object detection evaluations. Compared with the basal YOLOv5, SPH-YOLOv5 improves the mean Average Precision (mAP) by 0.071 on the DOTA dataset.
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