特征提取
计算机科学
棱锥(几何)
计算复杂性理论
特征(语言学)
人工智能
还原(数学)
图像(数学)
模式识别(心理学)
上下文图像分类
集合(抽象数据类型)
目标检测
故障检测与隔离
计算机视觉
算法
数学
语言学
哲学
几何学
执行机构
程序设计语言
作者
Jingwen Cheng,Jie Yuan,Xiaoning Hu,Bin Xie,Junrui Wang
摘要
In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity.
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