Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing

Wang, Junliang; Gao, Pengjie; Zhang, Jie; Lu, Chao; Shen, Bo

doi:10.1016/j.rcim.2022.102513

Cited by 16 publications

(2 citation statements)

References 33 publications

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“…Model augmentation methods are indispensable in object detection tasks, especially when datasets are limited. Wang et al made a significant contribution to hybrid defect detection in the field of computer vision through the utilisation of broad learning [25]. Broad learning, characterised by increasing the width of the network by augmenting the number of neurons in the hidden layers, enhances the model's expressive power.…”

Section: Transfer Learning In Yolomentioning

confidence: 99%

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

Wang,

Zhang,

Sun

et al. 2024

IET Collab Intel Manufact

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Object detection techniques have been widely used in power system equipment maintenance. However, in power systems, the accuracy of object detection is limited by the scarcity of publicly available datasets and the lack of scene pertinence. In order to solve these problems, an object detection method based on cloud data fusion and transfer learning (YOLO‐DFT) for power system equipment maintenance is proposed. Illustratively, YOLO‐DFT focuses on the object detection task involving birds and humans, generating a substantial and resilient human‐bird dataset through cloud‐based data fusion to compensate for the dearth of public datasets in the power system domain. By seamlessly integrating the YOLOv5 algorithm with a transfer learning strategy, a targeted detection mechanism for specific locations is meticulously formulated. The experimental results demonstrate that YOLO‐DFT effectively addresses object detection challenges in power systems, achieving a Mean Average Precision (MAP) measure of 0.925 across all classes, thereby providing a valuable reference for the maintenance of power system equipment.

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Section: Transfer Learning In Yolomentioning

confidence: 99%

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

Wang,

Zhang,

Sun

et al. 2024

IET Collab Intel Manufact

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“…The radical advancement of CV has numerous applications across a wide range of industries in the recent years. The diverse and impactful applications of CV are healthcare [10], security [11], entertainment [12], defense [13], self-driving vehicles [14], disaster relief and emergency [15], modern agriculture [16], banking industry [17], manufacturing industry [18] and robotics [19]. CV has evolved significantly over the years, with different techniques and approaches being developed to address the challenges of interpreting and understanding visual data [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Ramalingam,

Selvi,

Victor

et al. 2023

IEEE Access

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Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

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Assessing the 3D Position of a Car with a Single 2D Camera Using Siamese Networks

Yahia,

Lopes,

Bezerra

et al. 2024

Communications in Computer and Information Science

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Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing

Cited by 16 publications

References 33 publications

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Assessing the 3D Position of a Car with a Single 2D Camera Using Siamese Networks

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