计算机技术与发展2023，Vol.33Issue(12)：193-199,7.DOI:10.3969/j.issn.1673-629X.2023.12.027
迷彩伪装目标检测的视觉特征偏好研究
Research on Visual Feature Bias of Camouflaged Object Detection
摘要
Abstract
Camouflage uses designed color and texture patterns to disrupt the inherent shape of the target,so the visual features that its de-tection relies on should be different from those of conventional targets.However,the black box nature of convolutional neural networks makes it impossible to know the contribution of different visual features to model recognition.To solve this problem,a new visual feature decoupling method was designed based on the human visual system,which is suitable for camouflage scenes.This method decouples and analyzes the preference degree of object detection models on color,texture,and shape features.Specifically,an analysis architecture was used to eliminate a single feature while retaining the remaining features,and the performance degradation of the model was used as a measure of bias.Grayscale processing was used to eliminate the color features of images,region scrambling was used to disrupt the texture features of targets,and the inner shapes of targets were extracted to change their shape features.Experiments were conducted on publicly available datasets of camouflaged personnel and conventional personnel detection,respectively,and the results showed that the detection of camouflaged object mainly relies on texture,while the detection of conventional object mainly relies on shape.关键词
目标检测/迷彩伪装/特征解耦/人类视觉系统/卷积神经网络Key words
object detection/camouflage/feature decoupling/human visual system/convolutional neural networks分类
计算机与自动化引用本文复制引用
韩彤,曹铁勇,郑云飞,王杨,陈雷,王烨奎,付炳阳..迷彩伪装目标检测的视觉特征偏好研究[J].计算机技术与发展,2023,33(12):193-199,7.基金项目
国家自然科学基金青年科学基金(61801512) （61801512）
国家自然科学基金(62071484) （62071484）
江苏省优秀青年基金(BK20180080) （BK20180080）
