SMD Detection and Classification Using YOLO Network Based on Robust
                    Data Preprocessing and Augmentation Techniques

Fabrice, NDAYISHIMIYE; Lee, Joon Jae

doi:10.33851/jmis.2021.8.4.211

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Bhattacharya and Cloutier (2022) [14] also created a custom framework for fault detection, comparing it to various networks, including YOLO and faster region-based convolutional neural network, achieving mAP of 98.1%. Some of the authors focused on individual component classification, such as Ndayishimiye et al (2021) [15] and Yoon and Lee (2021) [16]. The authors both applied YOLO networks-versions 3 and 2, respectively.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Printed Circuit Boards Using YOLO Algorithm

et al. 2023

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Printed circuit boards (PCBs) are an indispensable part of every electronic device used today. With its computing power, it performs tasks in much smaller dimensions, but the process of making and sorting PCBs can be a challenge in PCB factories. One of the main challenges in factories that use robotic manipulators for “pick and place” tasks are object orientation because the robotic manipulator can misread the orientation of the object and thereby grasp it incorrectly, and for this reason, object segmentation is the ideal solution for the given problem. In this research, the performance, memory size, and prediction of the YOLO version 5 (YOLOv5) semantic segmentation algorithm are tested for the needs of detection, classification, and segmentation of PCB microcontrollers. YOLOv5 was trained on 13 classes of PCB images from a publicly available dataset that was modified and consists of 1300 images. The training was performed using different structures of YOLOv5 neural networks, while nano, small, medium, and large neural networks were used to select the optimal network for the given challenge. Additionally, the total dataset was cross validated using 5-fold cross validation and evaluated using mean average precision, precision, recall, and F1-score classification metrics. The results showed that large, computationally demanding neural networks are not required for the given challenge, as demonstrated by the YOLOv5 small model with the obtained mAP, precision, recall, and F1-score in the amounts of 0.994, 0.996, 0.995, and 0.996, respectively. Based on the obtained evaluation metrics and prediction results, the obtained model can be implemented in factories for PCB sorting applications.

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Section: Introductionmentioning

confidence: 99%

Detection and Classification of Printed Circuit Boards Using YOLO Algorithm

et al. 2023

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Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach

Teles,

Mendes,

Paiva

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Early disease detection of black gram plant leaf using cloud computing based YOLO V8 model

Motru,

P. Krishna,

A. Sudhir

2023

JIT

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Plant diseases pose a major threat to agricultural productivity and economies dependent on it. Monitoring plant growth and phenotypes is vital for early disease detection. In Indian agriculture, black-gram (Vigna mungo) is an important pulse crop afflicted by viral infections like Urdbean Leaf Crinkle Virus (ULCV), causing stunted growth and crinkled leaves. Such viral epidemics lead to massive crop losses and financial distress for farmers. According to the FAO, plant diseases cost countries $220 billion annually. Hence, there is a need for quick and accurate diagnosis of crop diseases like ULCV. Recent advances in computer vision and image processing provide promising techniques for automated non-invasive disease detection using leaf images. The key steps involve image pre-processing, segmentation, informative feature extraction, and training machine learning models for reliable classification. In this work, an automated ULCV detection system is developed using black gram leaf images. The Grey Level Co-occurrence Matrix (GLCM) technique extracts discriminative features from leaves. Subsequently, a deep convolutional neural network called YOLO (You Only Look Once) is leveraged to accurately diagnose ULCV based on the extracted features. Extensive experiments demonstrate the effectiveness of the GLCM-YOLO pipeline in identifying ULCV-infected leaves with high precision. Such automated diagnosis can aid farmers by providing early disease alerts, thereby reducing crop losses due to viral epidemics.

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SMD Detection and Classification Using YOLO Network Based on Robust Data Preprocessing and Augmentation Techniques

Cited by 4 publications

References 17 publications

Detection and Classification of Printed Circuit Boards Using YOLO Algorithm

Detection and Classification of Printed Circuit Boards Using YOLO Algorithm

Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach

Early disease detection of black gram plant leaf using cloud computing based YOLO V8 model

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