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Trending Object Detection Project Ideas & Topics in 2024 [For Freshers & Experienced]

Last updated:
2nd May, 2021
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Trending Object Detection Project Ideas & Topics in 2024 [For Freshers & Experienced]

Object Detection is a computer vision technique designed to oversee the identification and location of an object of specific classes in the image. Interpreting the object localisation can be done in various ways, including creating a bounding box around the object or marking every pixel in the image which contains the object (also known as segmentation).

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In the present article, we’ll cover the following topics:

  • Object detection projects
  • Advantages and disadvantages of object detection projects:
  • Online course on Data Science and ML:
  • Conclusion 

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Object Detection Projects

Below are five open-source object detection project ideas to improve your abilities in computer vision and image processing:

1. ImageAI

ImageAI is developed and maintained by the Olafenwa brothers. It is a DeepQuestAI project that is an open-source python library used to build applications and systems with self-contained Deep Learning and Computer Vision capabilities by using state-of-the-art Machine Learning Algorithms. It is developing using Python, OpenCV, Keras, and TensorFlow frameworks. 

It utilises RetinaNet, YOLOv3, and TinyYOLOv3 trained on the COCO dataset for object detection, video object detection, and object tracking. It also backs the image predictions using four different Machine Learning algorithms trained on the ImageNet-1000 dataset.

ImageAI also you to train custom models for object detection projects and object recognition of your articles using your custom object dataset. 

FYI: Free nlp course!

2. AI Basketball Analysis

AI Basketball Analysis is an Artificial Intelligence (AI) powered web app and API that analyses basketball shots and shooting pose built on top of the concept of object detection.

This project has primary three features: shot analysis shot detection, and detection API.

It implements this object detection project in Python using the open-source library OpenPose. The project is built using the concept of transfer learning, and the based model used for training is Faster-RCNN which is already pre-trained on the COCO dataset weights.


An aggregate view of object detection is a project designed for 3D Object detection for autonomous self-driving cars built on top of Python, OpenCV, and Tensorflow.

The dataset for 3D object detection is trained on Kitti Object Detection Dataset, and it compared the results to various other published methods on the Kitti 3D object and BCV Benchmarks. The Kitti dataset incorporates images of eight distinct classes, to be specific: Car, Van, Truck, Pedestrian, Person sitting, Cyclist, Tram, Misc, and DontCare.

4. NudeNet

NudeNet is a free and open-source neural nets project used to detect and classify nudity in an image or video stream and selective censoring.

The project is built in Python and Keras. A self hostable API service and a Python module are accessible for the immediate implementation of the project. The most recent version of Nudenet is trained on 160,000 auto-labeled images with an accuracy of 93%.

Here, one can upload a photo/video and classify them as:

  • Safe — Image/video is not sexually explicit.
  • Unsafe — Image/video is sexually explicit.

5. Vehicle Counting

Vehicle Counting is an open-source project which centres on vehicle detection, tracking, and counting. This object detection project also provides predictions for the speed, colour, size, and direction of the vehicle in real-time using TensorFlow Object Detection API.

Implementing this project uses TensorFlow, OpenCV, and python, and the model used for vehicle detection is SSD with mobilenet. Currently, this project can classify five vehicles: Bus, Car, Cycle, Truck, and Motorcycle.

Advantages and Disadvantages of Object Detection Projects

The Advantages

1. Improve Accuracy

The significant most advantage of object detection projects is that it is more accurate than human vision. The human brain is astounding, so much that it can finish pictures dependent on only a couple of snippets of data. But it can sometimes also keep us from seeing what is actually there. The complete picture isn’t always accurate because human brains make assumptions.

Object detection projects react to images based only on the data presented and not just snippets of it like the human brain. Although it can make assumptions based on patterns, it does not have the disadvantage of a human brain’s tendency to leap to conclusions that may not be accurate. 

Object detection also operates at the pixel level at which the human brain can’t process. This allows object detection projects to provide more accurate results. 

2. Deliver Faster Results

The human brain works fast and efficiently, but computers are better at multitasking, which permits object detection projects to deliver quicker results for some applications. Object detection projects can perform specific tasks for extended periods. 

Using object detection projects to finish projects not only delivers results in a fraction of the time but also frees up valuable time to focus on higher-level tasks that truly require human cognition. For instance, in a healthcare setting, using object detection projects to process X-ray images enables faster diagnosis, which potentially leads to speedy care delivery at critical times.

3. Reduce Costs

After an object detection project has been trained, it can repeat the same tasks with minimal cost, and it even continues to learn while it does that. This saves endless long hours of manual labour and its related expenses. 

Regardless of whether the resources saved by using object detection projects get allocated to people performing higher-level tasks or other expenses related to growing a business, this technology helps save money. 

4. Provide Unbiased Results

When object detection projects look at an image with a specific goal, it does not consider any information not related to that goal. This lessens the bias that humans might introduce to a process, whether intentionally or unintentionally.

5. Offer a Unique Customer Experience

Object detection projects have been used to improve the customer experience both online and in retail stores. Object detection can identify products or brands that an individual is most likely to buy via online platforms based on images in social media profiles. In grocery stores, Amazon Go has used object detection projects to revolutionise the shopping experience by detecting items in carts as people progress forward in the line and automatically charge them, eliminating long checkout lines. 

The Disadvantages 

One of the most controversial aspects of object detection projects is the potential for invasion of privacy. Facial recognition software is especially a contentious issue, particularly for individuals concerned about privacy invasion through surveillance online or in the actual world.

Online Course on Data Science and ML

Having a decent amount of theoretical knowledge is commendable, but implementing them in code in a real-time machine learning project is an entirely different thing. It is possible to get completely different and unexpected results based on various problems and datasets.

upGrad offers two relevant online courses, including:

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Topics that are Covered

Data Science Tool-Kit, Statistics and Exploratory Data Analytics, Machine Learning, Natural Language Processing, Deep Learning, Reinforcement Learning, and Deployment and Capstone Projects.

Who is this Course For?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, and Decision Scientist

Minimum Eligibility

Bachelor’s Degree with 50% or equivalent passing marks. Minimum one year of work experience or a degree in Mathematics or Statistics.

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After years of research by some top experts, Object Detection Projects are no longer a vision but a reality. The future of Object detection Projects and Object Detection Project Ideas is beyond our expectations. The scope of technology is booming with time, and with it is the need for experts. All you need are the right qualifications and skills to make you all acquainted with real-world experience and make you job-ready.

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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Frequently Asked Questions (FAQs)

1Which algorithm is best for object detection?

There are multiple good options. Some of them are listed below: VGG - It used to be the best one. The OpenCV implementation is a matter of great debate on the forums. YOLO - It has been in competition with R-CNN for a long time, but it still holds the crown. Mask RCNN - It is a refined version of R-CNN. Faster than the previous ones. Faster R-CNN - A simplified version of R-CNN. Faster than YOLO, but slower than Faster R-CNN. Faster R-CNN is currently the best algorithm for object detection.

2What is the need of object detection?

Object detection is usually done using a single image. It involves using image processing techniques to visualize the entire scene. Object detection is generally used in the field of autonomous vehicles, robotics and surveillance. The need of object detection is to identify and track characters and objects in images. There are many applications in which it is used widely.

3What is two stage object detection?

Two stage object detection and classification is a technique initially proposed by Ojala, Hariharan, and Lehtinen in 2001. The main advantage of the two stage detection method is its ability to perform detection and classification in one pass. It can be used to detect and classify objects of various types under different lighting and weather conditions. The two stage detection method is based on a two stage framework. The first stage is the characterization of the target object by using either a single classifier or a cascade of classifiers. The second stage is the non-maximum suppression of potential false alarms. The detection stage is followed by a classification stage.

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