Huaping Zhou , Tao Wu , Kelei Sun , Jin Wu , Bin Deng
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Thirdly, we also design a Laplacian convolution fixed-weight structure to enhance target boundary information, leading to the new Boundary Enhanced (BE) segmentation head. Finally, we design the Dynamic Weighted Hybrid Loss (DWH Loss), combining Dice loss, Focal loss, and BCE loss. It dynamically adjusts weights to balance multi-task optimization, further improving segmentation boundary clarity and overall performance. We conduct extensive experiments on the conveyor belt monitoring dataset and the COCO dataset. On the conveyor belt dataset, the AP<span><math><msub><mrow></mrow><mn>50</mn></msub></math></span> for the detection task reaches 98.4 %, and the AP<span><math><msub><mrow></mrow><mn>50</mn></msub></math></span> for the segmentation task reaches 73.5 %. These results outperform most state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103722"},"PeriodicalIF":15.5000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Task-oriented multi-scale dynamic feature fusion for robust conveyor belt monitoring\",\"authors\":\"Huaping Zhou , Tao Wu , Kelei Sun , Jin Wu , Bin Deng\",\"doi\":\"10.1016/j.inffus.2025.103722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Existing conveyor belt monitoring methods suffer from unreasonable multi-task feature allocation and limited boundary feature extraction capability. To address these issues, this study develops a novel information fusion framework integrating Mask R-CNN-based detection and segmentation for conveyor belt status monitoring. Firstly, we propose the Multi-Scale Dynamic Feature Fusion (MS-DFF) module. It uses a multi-stage parallel multi-scale convolution network and dynamic weight adjustment mechanism to flexibly fuse and optimize multi-scale features. Secondly, we propose the Task-Oriented Module (TOM). It optimizes task adaptability between the detection and segmentation branches, combining frequency domain and spatial-domain features to meet multi-task requirements. Thirdly, we also design a Laplacian convolution fixed-weight structure to enhance target boundary information, leading to the new Boundary Enhanced (BE) segmentation head. Finally, we design the Dynamic Weighted Hybrid Loss (DWH Loss), combining Dice loss, Focal loss, and BCE loss. It dynamically adjusts weights to balance multi-task optimization, further improving segmentation boundary clarity and overall performance. We conduct extensive experiments on the conveyor belt monitoring dataset and the COCO dataset. On the conveyor belt dataset, the AP<span><math><msub><mrow></mrow><mn>50</mn></msub></math></span> for the detection task reaches 98.4 %, and the AP<span><math><msub><mrow></mrow><mn>50</mn></msub></math></span> for the segmentation task reaches 73.5 %. 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引用次数: 0
摘要
现有的输送带监测方法存在多任务特征分配不合理、边界特征提取能力有限等问题。为了解决这些问题,本研究开发了一种新的信息融合框架,将基于Mask r - cnn的检测和分割集成到输送带状态监测中。首先,我们提出了多尺度动态特征融合(MS-DFF)模块。采用多阶段并行多尺度卷积网络和动态权值调整机制,灵活融合和优化多尺度特征。其次,我们提出了任务导向模块(TOM)。它优化了检测和分割分支之间的任务适应性,结合频域和空域特征,满足多任务需求。第三,我们还设计了一个拉普拉斯卷积定权结构来增强目标的边界信息,从而得到新的边界增强分割头。最后,我们设计了动态加权混合损耗(DWH损耗),结合了Dice损耗、Focal损耗和BCE损耗。动态调整权重以平衡多任务优化,进一步提高分割边界的清晰度和整体性能。我们对传送带监测数据集和COCO数据集进行了广泛的实验。在输送带数据集上,检测任务的AP50达到98.4%,分割任务的AP50达到73.5%。这些结果优于大多数最先进的方法。
Task-oriented multi-scale dynamic feature fusion for robust conveyor belt monitoring
Existing conveyor belt monitoring methods suffer from unreasonable multi-task feature allocation and limited boundary feature extraction capability. To address these issues, this study develops a novel information fusion framework integrating Mask R-CNN-based detection and segmentation for conveyor belt status monitoring. Firstly, we propose the Multi-Scale Dynamic Feature Fusion (MS-DFF) module. It uses a multi-stage parallel multi-scale convolution network and dynamic weight adjustment mechanism to flexibly fuse and optimize multi-scale features. Secondly, we propose the Task-Oriented Module (TOM). It optimizes task adaptability between the detection and segmentation branches, combining frequency domain and spatial-domain features to meet multi-task requirements. Thirdly, we also design a Laplacian convolution fixed-weight structure to enhance target boundary information, leading to the new Boundary Enhanced (BE) segmentation head. Finally, we design the Dynamic Weighted Hybrid Loss (DWH Loss), combining Dice loss, Focal loss, and BCE loss. It dynamically adjusts weights to balance multi-task optimization, further improving segmentation boundary clarity and overall performance. We conduct extensive experiments on the conveyor belt monitoring dataset and the COCO dataset. On the conveyor belt dataset, the AP for the detection task reaches 98.4 %, and the AP for the segmentation task reaches 73.5 %. These results outperform most state-of-the-art methods.
期刊介绍:
Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.