Open AccessVehicle Detection in Remote Sensing Image Based on Machine Vision
Author(s) -
Liming Zhou,
Chen Zheng,
Huijie Yan,
Xianyu Zuo,
Baojun Qiao,
Bing Zhou,
Minghu Fan,
Yang Liu
Publication year - 2021
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2021/8683226
Subject(s) - computer science , artificial intelligence , shadow (psychology) , computer vision , feature (linguistics) , convolution (computer science) , interference (communication) , image (mathematics) , object detection , convolutional neural network , deep learning , remote sensing , pattern recognition (psychology) , artificial neural network , telecommunications , psychology , linguistics , psychotherapist , geology , philosophy , channel (broadcasting)
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.