计算机科学
特征(语言学)
增采样
重新使用
人工智能
架空(工程)
目标检测
卷积神经网络
计算机视觉
遥感
图像(数学)
模式识别(心理学)
生态学
哲学
语言学
生物
地质学
操作系统
作者
Ling Tian,Yu Cao,Bokun He,Yifan Zhang,Chu He,Deshi Li
出处
期刊:Remote Sensing
[MDPI AG]
日期:2021-03-31
卷期号:13 (7): 1327-1327
被引量:32
摘要
As the application scenarios of remote sensing imagery (RSI) become richer, the task of ship detection from an overhead perspective is of great significance. Compared with traditional methods, the use of deep learning ideas has more prospects. However, the Convolutional Neural Network (CNN) has poor resistance to sample differences in detection tasks, and the huge differences in the image environment, background, and quality of RSIs affect the performance for target detection tasks; on the other hand, upsampling or pooling operations result in the loss of detailed information in the features, and the CNN with outstanding results are often accompanied by a high computation and a large amount of memory storage. Considering the characteristics of ship targets in RSIs, this study proposes a detection framework combining an image enhancement module with a dense feature reuse module: (1) drawing on the ideas of the generative adversarial network (GAN), we designed an image enhancement module driven by object characteristics, which improves the quality of the ship target in the images while augmenting the training set; (2) the intensive feature extraction module was designed to integrate low-level location information and high-level semantic information of different resolutions while minimizing the computation, which can improve the efficiency of feature reuse in the network; (3) we introduced the receptive field expansion module to obtain a wider range of deep semantic information and enhance the ability to extract features of targets were at different sizes. Experiments were carried out on two types of ship datasets, optical RSI and Synthetic Aperture Radar (SAR) images. The proposed framework was implemented on classic detection networks such as You Only Look Once (YOLO) and Mask-RCNN. The experimental results verify the effectiveness of the proposed method.
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