Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spa...Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spatial and semantic information.However,the performance of CNN-based methods remains limited in classification accuracy,primarily due to insufficient exploration of local image characteristics.Unlike CNNs,Vision Transformer(ViT)captures discriminative features by modeling relationships between local image patches.However,such methods typically require a large number of training samples to perform effectively.In the context of foreign body classification on coal conveyor belts,the limited availability of training samples hinders the full exploitation of Vision Transformer’s(ViT)capabilities.To address this issue,we propose an efficient approach,termed Key Part-level Attention Vision Transformer(KPA-ViT),which incorporates key local information into the transformer architecture to enrich the training information.It comprises three main components:a key-point detection module,a key local mining module,and an attention module.To extract key local regions,a key-point detection strategy is first employed to identify the positions of key points.Subsequently,the key local mining module extracts the relevant local features based on these detected points.Finally,an attention module composed of self-attention and cross-attention blocks is introduced to integrate global and key part-level information,thereby enhancing the model’s ability to learn discriminative features.Compared to recent transformer-based frameworks—such as ViT,Swin-Transformer,and EfficientViT—the proposed KPA-ViT achieves performance improvements of 9.3%,6.6%,and 2.8%,respectively,on the CUMT-BelT dataset,demonstrating its effectiveness.展开更多
近年来,随着图像编辑技术的不断发展,数字图像的知识产权保护形势日益严峻。为有效遏制图像内容的非法窃取、复制与篡改行为,切实保障用户的合法权益,图像拷贝检测作为内容审核体系的关键组成部分,其重要性不言而喻。其中,一种常见的规...近年来,随着图像编辑技术的不断发展,数字图像的知识产权保护形势日益严峻。为有效遏制图像内容的非法窃取、复制与篡改行为,切实保障用户的合法权益,图像拷贝检测作为内容审核体系的关键组成部分,其重要性不言而喻。其中,一种常见的规避检测手段是攻击者将源图像的部分内容巧妙地植入目标图像中,形成“画中画”式的局部拷贝。针对这一挑战,本文提出了一种基于ViT的局部图像拷贝检测算法。该算法旨在利用ViT强大的局部特征提取能力,在原始全局特征的基础上提取出图像的局部特征,揭示图像间潜在的依赖关系,并通过重新排列相关图像的顺序,提高具有更高相似度的图像在检测结果中的排序,从而精准实现对图像局部信息的拷贝检测。本文与基于自监督描述符的图像拷贝检测算法(A Self-Supervised Descriptor for Image Copy Detection)相比,在DISC2021数据集上取得显著提升,局部拷贝图像识别准确率提高10%,μAP(Micro Average Precision)指标提高10%~15%。此外,本文还利用热力图进行了直观的定性分析,证实了ViT能够敏锐地捕捉到图像中存在局部拷贝的区域,进一步验证了算法的有效性。本文提出的基于ViT的局部图像拷贝检测重排序算法能对使用画中画图像增强方式的拷贝图像进行有效检测,并在DISC2021数据集上取得了优异成绩,拓展了数字图像取证领域研究的新思路。展开更多
基金funded by the National Key Research and Development Program of China(grant number 2023YFC2907600)the National Natural Science Foundation of China(grant number 52504132)Tiandi Science and Technology Co.,Ltd.Science and Technology Innovation Venture Capital Special Project(grant number 2023-TD-ZD011-004).
文摘Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems.Previous approaches have primarily utilized convolutional neural networks(CNNs)to effectively integrate spatial and semantic information.However,the performance of CNN-based methods remains limited in classification accuracy,primarily due to insufficient exploration of local image characteristics.Unlike CNNs,Vision Transformer(ViT)captures discriminative features by modeling relationships between local image patches.However,such methods typically require a large number of training samples to perform effectively.In the context of foreign body classification on coal conveyor belts,the limited availability of training samples hinders the full exploitation of Vision Transformer’s(ViT)capabilities.To address this issue,we propose an efficient approach,termed Key Part-level Attention Vision Transformer(KPA-ViT),which incorporates key local information into the transformer architecture to enrich the training information.It comprises three main components:a key-point detection module,a key local mining module,and an attention module.To extract key local regions,a key-point detection strategy is first employed to identify the positions of key points.Subsequently,the key local mining module extracts the relevant local features based on these detected points.Finally,an attention module composed of self-attention and cross-attention blocks is introduced to integrate global and key part-level information,thereby enhancing the model’s ability to learn discriminative features.Compared to recent transformer-based frameworks—such as ViT,Swin-Transformer,and EfficientViT—the proposed KPA-ViT achieves performance improvements of 9.3%,6.6%,and 2.8%,respectively,on the CUMT-BelT dataset,demonstrating its effectiveness.
文摘近年来,随着图像编辑技术的不断发展,数字图像的知识产权保护形势日益严峻。为有效遏制图像内容的非法窃取、复制与篡改行为,切实保障用户的合法权益,图像拷贝检测作为内容审核体系的关键组成部分,其重要性不言而喻。其中,一种常见的规避检测手段是攻击者将源图像的部分内容巧妙地植入目标图像中,形成“画中画”式的局部拷贝。针对这一挑战,本文提出了一种基于ViT的局部图像拷贝检测算法。该算法旨在利用ViT强大的局部特征提取能力,在原始全局特征的基础上提取出图像的局部特征,揭示图像间潜在的依赖关系,并通过重新排列相关图像的顺序,提高具有更高相似度的图像在检测结果中的排序,从而精准实现对图像局部信息的拷贝检测。本文与基于自监督描述符的图像拷贝检测算法(A Self-Supervised Descriptor for Image Copy Detection)相比,在DISC2021数据集上取得显著提升,局部拷贝图像识别准确率提高10%,μAP(Micro Average Precision)指标提高10%~15%。此外,本文还利用热力图进行了直观的定性分析,证实了ViT能够敏锐地捕捉到图像中存在局部拷贝的区域,进一步验证了算法的有效性。本文提出的基于ViT的局部图像拷贝检测重排序算法能对使用画中画图像增强方式的拷贝图像进行有效检测,并在DISC2021数据集上取得了优异成绩,拓展了数字图像取证领域研究的新思路。
