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Detection of Small Size Construction Tools Dataset

28687122103
Tagconstruction
Taskobject detection
Release YearMade in 2022
LicenseCC BY 4.0
Download132 GB

Introduction #

Released 2022-12-12 ·Kanghyeok Lee, Jungeun Hwang, Chanwoong Jeonet al.

The authors of the Detection of Small Size Construction Tools dataset have explored the application of deep-learning techniques to object detection within the construction industry. Unlike previous studies that predominantly focused on outdoor sites and larger equipment, their emphasis was on enhancing safety by detecting small tools commonly used in indoor construction. Recognizing the need for robust datasets to develop effective object detection models for such tools, they undertook the creation of a comprehensive image database.

The dataset is tailored for training object detection models for small-sized construction tools. It encompasses 12 distinct classes of target tools that are typically utilized in indoor construction scenarios. These tools include bucket, cutter, drill, grinder, hammer, knife, saw, shovel, spanner, tacker, trowel, and wrench. The dataset is composed of a substantial collection of 25,084 images, each paired with corresponding label data to facilitate supervised learning.

The images within the dataset were curated with a keen focus on the diversity of the objects. This diversity was achieved by capturing tools of varying shapes, sizes, and colors. The authors also introduced variations in image characteristics, such as resolution, lighting, occlusion, and background, to improve the model’s robustness. Of the total images in the dataset, 6258 (25%) were directly captured from real construction sites, contributing to the dataset’s authenticity and relevance.

Object annotations for each image were meticulously executed using bounding boxes, which were subsequently saved in text files. The bounding box coordinates were structured as follows: Class, Center X, Center Y, Width, and Height. The “Class” parameter denoted one of the 12 construction tool types, while “Center X” and “Center Y” represented the center coordinates of the bounding box, normalized with respect to the image resolution. “Width” and “Height” indicated the dimensions of the bounding box, also normalized to the image resolution. This comprehensive dataset, meticulously curated and annotated, stands to enable more accurate and effective object detection models for small construction tools in indoor settings.

Note, that the presented version of the dataset has more images than it was claimed in orifinal dataset. This is due to the fact that some images have duplicates because they contain both train and test labels. Moreover, there is an additional split undefined which contains images with no pre-defined splits in the original dataset.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Image Dataset for Object Detection of Small Size Construction Tools is a dataset for an object detection task. It is used in the construction industry.

The dataset consists of 28687 images with 49189 labeled objects belonging to 12 different classes including trowel, drill, grinder, and other: wrench, hammer, saw, tacker, cutter, spanner, bucket, knife, and shovel.

Images in the Detection of Small Size Construction Tools dataset have bounding box annotations. There are 5636 (20% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (14187 images), test (8819 images), and undefined (5681 images). The dataset was released in 2022 by the Korea Expressway Corporation and Inha University, Korea.

Dataset Poster

Explore #

Detection of Small Size Construction Tools dataset has 28687 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
OpenSample annotation mask from Detection of Small Size Construction ToolsSample image from Detection of Small Size Construction Tools
👀
Have a look at 28687 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 12 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 12
Class
Images
Objects
Count on image
average
Area on image
average
trowel
rectangle
4463
8762
1.96
7.97%
drill
rectangle
4348
6650
1.53
9%
grinder
rectangle
3715
5853
1.58
12.91%
wrench
rectangle
3294
5604
1.7
8.34%
hammer
rectangle
3256
4954
1.52
10.1%
saw
rectangle
2773
4249
1.53
5.62%
tacker
rectangle
2332
3545
1.52
5.56%
cutter
rectangle
1439
2418
1.68
14.18%
spanner
rectangle
1400
1494
1.07
4.88%
bucket
rectangle
1208
1952
1.62
9.75%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 12
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
trowel
rectangle
8762
4.07%
66.47%
0.05%
36px
1.38%
3624px
99.61%
648px
20.82%
50px
1.08%
4978px
94.58%
drill
rectangle
6650
5.89%
76.35%
0.05%
19px
1.76%
3259px
94.98%
653px
21.93%
36px
1.15%
5307px
99.21%
grinder
rectangle
5853
8.23%
72.89%
0.05%
24px
1.86%
3597px
99.58%
781px
27.09%
34px
1.62%
4854px
96.16%
wrench
rectangle
5604
4.93%
51.42%
0.02%
34px
1.42%
3487px
99.81%
646px
22.91%
33px
1.06%
4014px
92.89%
hammer
rectangle
4954
6.66%
65.21%
0.08%
29px
2.69%
3197px
100%
779px
26.52%
39px
1.12%
4794px
92.26%
saw
rectangle
4249
3.67%
69.57%
0.02%
22px
1.31%
3564px
99.77%
397px
17.17%
35px
1.6%
4383px
88.47%
tacker
rectangle
3545
3.66%
89.8%
0%
11px
0.2%
3430px
100%
468px
16.87%
1px
0.03%
4928px
91.84%
knife
rectangle
2647
2%
25.24%
0.07%
49px
1.88%
2574px
83.92%
448px
13.29%
60px
1.32%
2509px
81.55%
cutter
rectangle
2418
8.52%
92.9%
0.22%
59px
5.46%
3645px
99.84%
839px
32.05%
66px
3.44%
5215px
95.03%
bucket
rectangle
1952
6.23%
71.73%
0.04%
51px
2.73%
3798px
94.2%
761px
25.56%
54px
1.51%
2937px
92.23%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 49189 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 49189
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
spanner
rectangle
ODt.19438.jpg
2600 x 4624
1488px
57.23%
1444px
31.23%
17.87%
2
wrench
rectangle
ODt.7937.jpg
3088 x 5488
510px
16.52%
858px
15.63%
2.58%
3
wrench
rectangle
ODt.7937.jpg
3088 x 5488
771px
24.97%
341px
6.21%
1.55%
4
spanner
rectangle
ODt.7937.jpg
3088 x 5488
683px
22.12%
455px
8.29%
1.83%
5
hammer
rectangle
ODt.2349.jpg
3024 x 4032
1134px
37.5%
801px
19.87%
7.45%
6
hammer
rectangle
ODt.2349.jpg
3024 x 4032
1010px
33.4%
410px
10.17%
3.4%
7
drill
rectangle
ODt.4876.jpg
5488 x 3088
694px
12.65%
585px
18.94%
2.4%
8
saw
rectangle
ODt.4876.jpg
5488 x 3088
594px
10.82%
422px
13.67%
1.48%
9
tacker
rectangle
ODt.17043.jpg
1080 x 1920
245px
22.69%
465px
24.22%
5.49%
10
tacker
rectangle
ODt.17043.jpg
1080 x 1920
51px
4.72%
87px
4.53%
0.21%

License #

Image Dataset for Object Detection of Small Size Construction Tools is under CC BY 4.0 license.

Source

Citation #

If you make use of the Detection of Small Size Construction Tools data, please cite the following reference:

@misc{kanghyeok_lee_2022_6530106,
    author       = {Kanghyeok Lee and
                    Jungeun Hwang and
                    Chanwoong Jeon and
                    May Mo Eizan and
                    Arnold Jan Bitangjol and
                    Do Hyoung Shin},
    title        = {{Image Dataset for Object Detection of Small Size 
                      Construction Tools}},
    month        = dec,
    year         = 2022,
    note         = {{This work was supported by the National Research 
                      Foundation of Korea (NRF) grant funded by the
                      Korea government (MEST)  (No.NRF -
                      2019R1A2C1088824)}},
    publisher    = {Zenodo},
    version      = {1.0},
    doi          = {10.5281/zenodo.6530106},
    url          = {https://doi.org/10.5281/zenodo.6530106}
}

Source

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@misc{ visualization-tools-for-small-size-construction-tools-dataset,
  title = { Visualization Tools for Detection of Small Size Construction Tools Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/small-size-construction-tools } },
  url = { https://datasetninja.com/small-size-construction-tools },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Detection of Small Size Construction Tools can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Detection of Small Size Construction Tools', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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