Progress of Machine Vision in the Detection of Cancer Cells in Histopathology

He, Wenbin; Han, Yongjie; Ming, Wuyi; Du, Jinguang; Liu, Yinxia; Yang, Yuan; Wang, Leijie; Wang, Yongqiang; Jiang, Zhiwen; Cao, Chen; Yuan, Jie

doi:10.1109/access.2022.3161575

Cited by 6 publications

(2 citation statements)

References 99 publications

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“…Within the industrial sector, these algorithms find utility in processing and positioning tasks [13], [14], while also capable of identifying defects in 3C products [15]- [17]. Similarly, within the medical field, deep learning-based algorithms aid doctors in analyzing medical images for diagnostic support [18], [19]. In agriculture, these algorithms contribute to crop monitoring [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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A printed circuit board (PCB) functions as a substrate essential for interconnecting and securing electronic components. Its widespread integration is evident in modern electronic devices, spanning computers, cell phones, televisions, digital cameras, and diverse apparatus. Ensuring product quality mandates meticulous defect inspection, a task exacerbated by the heightened precision of contemporary circuit boards, intensifying the challenge of defect detection. Conventional algorithms, hampered by inefficiency and limited accuracy, fall short of usage benchmarks. In contrast, PCB defect detection algorithms rooted in deep learning hold promise for achieving heightened accuracy and efficiency, bolstered by their adeptness at discerning novel defect types. This review presents a comprehensive analysis of machine vision-based PCB defect detection algorithms, traversing the realms of machine learning and deep learning. It commences by contextualizing and elucidating the significance of such algorithms, followed by an extensive exploration of their evolution within the machine vision framework, encompassing classification, comparison, and analysis of algorithmic principles, strengths, and weaknesses. Moreover, the introduction of widely used PCB defect detection datasets and assessment indices enhances the evaluation of algorithmic performance. Currently, the detection accuracy can exceed 95% at an Intersection over Union (IoU) of 0.5. Lastly, potential future research directions are identified to address the existing issues in the current algorithm. These directions include utilizing Transformers as a foundational framework for creating new algorithms and employing techniques like Generative Adversarial Networks (GANs) and reinforcement learning to enhance PCB defect detection performance.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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“…YOLOv3 [ 23 ] predicts the coordinates of the possible bounding box, object class, and confidence of object class. It has been widely used in vehicle [ 24 ], pedestrian [ 25 , 26 ], fire [ 27 ], and even medical cell detections [ 28 , 29 ], etc. However, the huge computation cost of YOLOv3 makes it difficult to achieve real-time detection in small airborne UAV systems.…”

Section: Introductionmentioning

confidence: 99%

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Tang

Jin

Liu

et al. 2023

Sensors

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The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.

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Superwettable interface towards biodetection in confined space

Pang,

Cao,

et al. 2023

Nano Res.

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Progress of Machine Vision in the Detection of Cancer Cells in Histopathology

Cited by 6 publications

References 99 publications

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

Superwettable interface towards biodetection in confined space

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