ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data
In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present “ElectroCom61,” a comprehensive multi-class ob...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000630 |
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| author | Md. Faiyaz Abdullah Sayeedi Anas Mohammad Ishfaqul Muktadir Osmani Taimur Rahman Jannatul Ferdous Deepti Raiyan Rahman Salekul Islam |
| author_facet | Md. Faiyaz Abdullah Sayeedi Anas Mohammad Ishfaqul Muktadir Osmani Taimur Rahman Jannatul Ferdous Deepti Raiyan Rahman Salekul Islam |
| author_sort | Md. Faiyaz Abdullah Sayeedi |
| collection | DOAJ |
| description | In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present “ElectroCom61,” a comprehensive multi-class object detection dataset encompassing 61 commonly used electronic components. Our dataset, sourced from the electronic components collection at United International University (UIU) in Dhaka, Bangladesh, comprises 2121 meticulously annotated images. We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. Additionally, we conducted minimal pre-processing to optimise model training and performance. “ElectroCom61” stands as a valuable asset for developing cutting-edge electronic component detection systems, with far-reaching applications in both education and industry. Its potential applications span from interactive educational tools to e-waste management systems and streamlined inventory management processes in electronic manufacturing and automation. The code for technical validation of this dataset is available on GitHub: https://github.com/faiyazabdullah/ElectroCom61 |
| format | Article |
| id | doaj-art-b76c684934f2416981d37d12c7785181 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-b76c684934f2416981d37d12c77851812025-02-06T05:11:57ZengElsevierData in Brief2352-34092025-04-0159111331ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley DataMd. Faiyaz Abdullah Sayeedi0Anas Mohammad Ishfaqul Muktadir Osmani1Taimur Rahman2Jannatul Ferdous Deepti3Raiyan Rahman4Salekul Islam5Department of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Electrical and Computer Engineering, North South University, Bangladesh; Corresponding author.In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present “ElectroCom61,” a comprehensive multi-class object detection dataset encompassing 61 commonly used electronic components. Our dataset, sourced from the electronic components collection at United International University (UIU) in Dhaka, Bangladesh, comprises 2121 meticulously annotated images. We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. Additionally, we conducted minimal pre-processing to optimise model training and performance. “ElectroCom61” stands as a valuable asset for developing cutting-edge electronic component detection systems, with far-reaching applications in both education and industry. Its potential applications span from interactive educational tools to e-waste management systems and streamlined inventory management processes in electronic manufacturing and automation. The code for technical validation of this dataset is available on GitHub: https://github.com/faiyazabdullah/ElectroCom61http://www.sciencedirect.com/science/article/pii/S2352340925000630Electronic componentDeep learningImage processingObject detectionComputer visionE-waste management |
| spellingShingle | Md. Faiyaz Abdullah Sayeedi Anas Mohammad Ishfaqul Muktadir Osmani Taimur Rahman Jannatul Ferdous Deepti Raiyan Rahman Salekul Islam ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data Data in Brief Electronic component Deep learning Image processing Object detection Computer vision E-waste management |
| title | ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data |
| title_full | ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data |
| title_fullStr | ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data |
| title_full_unstemmed | ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data |
| title_short | ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data |
| title_sort | electrocom61 a multiclass dataset for detection of electronic componentsmendeley data |
| topic | Electronic component Deep learning Image processing Object detection Computer vision E-waste management |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000630 |
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