Five Unbelievable Open-Source Object Detection Projects Ready to Use in 2022

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Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos.

In todayā€™s article, we are going to talk about five of the open-source Object Detection projects to enhance your skills in the field of computer vision and image processing.

Note: In this article we are going to talk about some of the not-so-famous but really good open-source projects which you can use in your projects. To read more about each of them I recommend following the link given along the project.


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Coming back to the topic -

1.Imageai

Imageai**Ā is aĀ DeepQuestAI project. It is aĀ free, open-source Python library used to build applications and systems with self-containedĀ Deep LearningĀ andĀ Computer VisionĀ capabilities with state-of-the-artĀ Machine Learning Algorithms. It is made usingĀ *Python, OpenCV, Keras, and TensorFlow*Ā frameworks.

It usesĀ RetinaNet, YOLOv3,*,* and TinyYOLOv3**Ā trained on theĀ COCO datasetĀ forĀ *object detection, video object detection,, and object tracking*. It also supportsĀ image predictionsĀ usingĀ four different Machine LearningĀ algorithms trained on theĀ ImageNet-1000Ā dataset.

The best part ofĀ ImageaiĀ is that it allows you to train custom models for object detection and object recognition of your objects using your custom object dataset.

Currently, Imageai is Developed and Maintained byĀ Moses OlafenwaĀ andĀ John Olafenwa, brothers

Image Source:Ā https://github.com/OlafenwaMoses/ImageAI


2.AVOD

Aggregate View Object DetectionĀ is a project designed forĀ 3D Object Detection for autonomous self-drivingĀ cars built on top ofĀ Python, OpenCV, and TensorFlow.

The authors trained the dataset for 3D object detection onĀ Kitti Object Detection Dataset,Ā and we compared the results with various other published methods on theĀ *Kitti 3DĀ object*Ā andĀ *BCVĀ Benchmarks*. TheĀ Kitti datasetĀ contains images of 8 different classes:Ā Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc, and DontCare.

Source:Ā https://github.com/kujason/avod


3.Nudenet

NudenetĀ is aĀ free and open-source Neural Nets project used to detect and classifyĀ nudityĀ in anĀ image or video streamĀ andĀ selective censoring.

This project is built inĀ Python and Keras. Self-hostableĀ API service andĀ aĀ Python module areĀ available for the direct implementation of the project. The latest version ofĀ NudenetĀ is trained onĀ 160,000Ā *auto-labelled images with anĀ accuracy of 93%*.

Installing theĀ NudenetĀ libraryĀ pip install nudenet, you canĀ upload a photo/video and classify the image as:

  • Safe ā€” Image/video is not sexually explicit
  • Unsafe ā€” Image/video is sexually explicit

Source: An uncensored version of the following image can be found atĀ https://i.imgur.com/rga6845.jpgĀ (NSFW)


4. AI Basketball Analysis

AI Basketball Analysis** is anĀ Artificial intelligence-powered**,**Ā web appĀ andĀ APIĀ that you can use toĀ analyze basketball shotsĀ andĀ shooting posesĀ built on top of the concept ofĀ object detection.

This project has mainly three features ā€” shot analysis, shot detection, and detection API.

This project is implemented inĀ PythonĀ using an open-source library,Ā OpenPose (used to calculate the angle of elbow and knee). This project is built using the concept ofĀ transfer learning,Ā and the based model used for training isĀ Faster-RCNNĀ which is pre-trained on theĀ COCO dataset weights.

Source:Ā https://ai-basketball-analysis.herokuapp.com/


5. Vehicle Counting

Vehicle CountingĀ is anĀ open-source projectĀ that focuses onĀ Vehicle Detection, Tracking, and Counting. This project also providesĀ predictionsĀ for the vehicle''sĀ speed, colour, size, and direction in real-time usingĀ TensorFlow Object Detection API.

This project is implemented usingĀ Tensorflow, OpenCV, and Python,Ā and the model used forĀ vehicle detectionĀ isĀ SSD with Mobilenet. Currently, this project is capable of classifying* five vehicles:Ā Bus, Car, Cycle, Truck, and Motorcycle.*

Source:Ā https://github.com/ahmetozlu/vehicle_counting_tensorflow


References

  1. Imageai
@misc {ImageAI,
    author = "Moses and John Olafenwa",
    title  = "ImageAI, an open source python library built to   empower developers to build applications and systems  with self-contained Computer Vision capabilities",
    url    = "https://github.com/OlafenwaMoses/ImageAI",
    month  = "mar",
    year   = "2018--"
}

2. AVOD

3. Nudenet

4. AI Basketball Analysis

5. Vehicle Counting

@ONLINE{vdtct,
    author = "Ahmet ƖzlĆ¼",
    title  = "Vehicle Detection, Tracking and Counting by TensorFlow",
    year   = "2018",
    url    = "https://github.com/ahmetozlu/vehicle_counting_tensorflow"
}

IfĀ you enjoyed reading this article, I am sure that we share similar interests and are/will be in similar industries. So letā€™s connect viaĀ LinkedInĀ andĀ Github. Please do not hesitate to send a contact request!

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