This project is trying to create an efficient computer or machine vision model to detect different kinds of construction equipment in construction sites and we are starting with three classes which are excavators, trucks, and wheel loaders.

The dataset is provided by Mohamed Sabek, a Spring 2022 Master of Science graduate from Arizona State University in Construction Management and Technology.

The raw images (v1) contains:

1. 1,532 annotated examples of "excavators"
2. 1,269 annotated examples of "dump truck"
3. 1,080 annotated examples of "wheel loader"

Note: versions 2 and 3 (v2 and v3) contain the raw images resized at 416 by 416 (stretch to) and 640 by 640 (stretch to) without any augmentations.

Personal Protective Equipment Dataset and Model

This dataset is a collection of images that contains annotations for the classes below:

• goggles
• helmet
• mask
• no-suit
• no_goggles
• no_helmet
• no_mask
• no_shoes
• shoes
• suit
• no_glove
• glove

Usage

Most of these classes are underrepresented and would need to be balanced for better detection. An improved model can be utilized for use cases that'll detect the classes above in order to minimize exposure to hazards that cause serious workplace injuries.

This dataset contains images of 3D printer failures across a variety of print jobs spanning several types of printer, material, models, and colors. It can be used as the starting point for creating a computer vision model that monitors a print job and aborts it as soon as it becomes evident that there is a problem (and alerts you before wasting a ton of time and money on materials). It can also be used to do automated quality assurance on finished models to make sure they do not exhibit common defects.

A dataset of 276 images is provided, along with a pre-trained model you can try in your browser and deploy to several different edge devices.

About this Dataset

This dataset was created by exporting images from images.cv and labeling them as an object detection dataset. The dataset contains 421 raw images (v1 - raw-images) and labeled classes include:

• forklift
• person

Background Information

This dataset was curated and annotated by Mohamed Attia.

The original dataset (v1) is composed of 451 images of various pills that are present on a large variety of surfaces and objects.

The dataset is available under the Public License.

Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset Versions

Version 1 (v1) - 451 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 1,083 images

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
• Augmentations:
  90° Rotate: Clockwise, Counter-Clockwise, Upside Down
  Crop: 0% Minimum Zoom, 77% Maximum Zoom
  Rotation: Between -45° and +45°
  Shear: ±15° Horizontal, ±15° Vertical
  Hue: Between -22° and +22°
  Saturation: Between -27% and +27%
  Brightness: Between -33% and +33%
  Exposure: Between -25% and +25%
  Blur: Up to 3px
  Noise: Up to 5% of pixels
  Cutout: 3 boxes with 10% size each
  Mosaic: Applied
  Bounding Box: Brightness: Between -25% and +25%
• Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
  • mAP = 91.4%, precision = 61.1%, recall = 93.9%

Version 3 (v3) - 1,083 images

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
• Augmentations:
  90° Rotate: Clockwise, Counter-Clockwise, Upside Down
  Crop: 0% Minimum Zoom, 77% Maximum Zoom
  Rotation: Between -45° and +45°
  Shear: ±15° Horizontal, ±15° Vertical
  Hue: Between -22° and +22°
  Saturation: Between -27% and +27%
  Brightness: Between -33% and +33%
  Exposure: Between -25% and +25%
  Blur: Up to 3px
  Noise: Up to 5% of pixels
  Cutout: 3 boxes with 10% size each
  Mosaic: Applied
  Bounding Box: Brightness: Between -25% and +25%
• Training Metrics: Trained from "scratch" (no transfer learning employed) on Roboflow
  • mAP = 84.3%, precision = 53.2%, recall = 86.7%

Version 4 (v4) - 451 images

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 5 (v5) - 496 images

• Preprocessing: Auto-Orient, all classes remapped (Modify Classes) to "pill", Isolate Objects
  • The Isolate Objects preprocessing step was added to convert this object detection project into a suitable format for export in OpenAI's CLIP annotation format so that it could be used as a classifcation model (classification dataset available here: https://universe.roboflow.com/mohamed-attia-e2mor/pill-classification)

Mohamed Attia - LinkedIn

Background Information

This dataset was curated and annotated by Mohamed Attia.

The original dataset (v1) is composed of 451 images of various pills that are present on a large variety of surfaces and objects.

The dataset is available under the Public License.

Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset Versions

Version 1 (v1) - 496 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 1,190 images

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"

• Augmentations:
  Outputs per training example: 3
  90° Rotate: Clockwise, Counter-Clockwise, Upside Down
  Shear: ±5° Horizontal, ±5° Vertical
  Hue: Between -25° and +25°
  Saturation: Between -10% and +10%
  Brightness: Between -10% and +10%
  Exposure: Between -10% and +10%
  Noise: Up to 2% of pixels
  Cutout: 5 boxes with 5% size each

• Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow

NOTE:

The Isolate Objects preprocessing step was added to convert the original object detection project into a suitable format for export in OpenAI's CLIP annotation format so that it could be used as a classifcation model in this project.

Mohamed Attia - LinkedIn

This project was created for research work by

Dainius Varna and Vytautas Abromavičius of Vilnius Gediminias Technical University in Lithuania.

The dataset consists of images of SMD-type electronic components, which are moving on a conveyor belt. There are four types of components in the collected dataset:

1. Capacitors
2. Resistors
3. Diodes
4. Transistors

This is the initial dataset that was augmented to create the final model. Download the raw-images dataset version (v2) of this project to start your own custom project.

• A research paper on the project, titled A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt was published in the MDPI Applied Sciences journal on May 31, 2022.

Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), 03227 Vilnius, Lithuania; vgtu@vgtu.lt

• Correspondence: vytautas.abromavicius@vilniustech.lt

Dataset Collection

The dataset was collected using Nvidia Data Capture Control.

Figure 3. Four types of electronic components used for the dataset. (a) capacitor, (b) resistor, (c) diode, (d) transistor.

Abstract:

The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor.

Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems.

This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components.

The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection.

The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform.

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a 75/25 train-test split.

Example Image:

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork or Download this Dataset button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify Classes

• v1 (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations
• v2 (raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original images
• v3 (v3): generated with the original 75/25 train-test split | Modify Classes used to drop person class | Preprocessing and Augmentation applied
• v5 (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class
• v8 (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and person classes
• v9 (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and helmet classes
• v10 (raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original images
• v11 (augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Model
• v12 (augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Fast Model
• v13 (augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Accurate Model
• v14 (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class, and remap/relabel helmet class to head

Choosing Between Computer Vision Model Sizes | Roboflow Train

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Recyclable Items Dataset and Model

This is an object detection dataset that contains the classes below:

• Plastic
• Glass
• Metal

Usage

This model can be potentially used to detect the objects above in an effort to sort them in a recycling center or an Automated River Cleaning System that uses computer vision.

Garbage Object-Detection to Identify Disposal Class

This dataset detects various kinds of waste, labeling with a class that indentifies how it should be disposed