计算机科学
人工智能
影子(心理学)
计算机视觉
特征(语言学)
卷积(计算机科学)
干扰(通信)
图像(数学)
目标检测
卷积神经网络
深度学习
遥感
模式识别(心理学)
人工神经网络
电信
语言学
频道(广播)
地质学
哲学
心理治疗师
心理学
作者
Liming Zhou,Chen Zheng,Huijie Yan,Xianyu Zuo,Baojun Qiao,Bing Zhou,Minghu Fan,Yang Liu
摘要
Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.
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