IEEE Deep Learning Project based projects for Mtech,Btech, BE, MS and diploma students in Trichy

Deep Learning Project for Engineering Students

IEEE Deep Learning Project based projects for Mtech,Btech, BE, MS and diploma students in Trichy

The scope of Deep Learning is playing a significant role in solving several challenges and is being implemented in various sectors as a result of the development of new artificial intelligence technologies. In other words, the human brain's biological neural network is what this technology tries to replicate. The practical approach will be more important for engineering students than theoretical understanding; we have a long list of projects that cover all application areas in a real-world setting. Let's look at some fascinating real-world deep learning project ideas that both professionals and amateurs can work on to test their expertise and gain more practical deep learning experience.

A kind of machine learning called deep learning uses artificial neural networks that are organised hierarchically to carry out particular ML tasks. Deep Learning networks learn from unstructured or unlabeled data using the unsupervised learning method. Artificial neural networks have a structure resembling a web of interconnected neuron nodes, precisely like the human brain.

Deep learning projects and examples in real life:

As new advances are being made in this domain, The more deep learning project ideas you try, the more experience you gain.

IEEE Deep learning projects

  •  FallWatch Real-time Monitoring for Fall Incidents
  •  Infrastructure Under the Microscope: A Road Pathology Investigation
  •  Safeguarding Oceans through Automated Oil Spill Detection
  •  Aquatic environment maintenance: deep learning-driven aquarium water management for fish welfare
  •  AccidentAware Tech Harnessing Deep Learning for Proactive Safety Monitoring
  •  SunShield Protecting Solar Panel Efficiency through Crack Detection
  •  SecureShield Shielding Public Spaces with Weapon Detection
  •  Safeguarding Natural Sand Resources from Theft
  •  SwiftGuard Smart Systems for Improved Zone-Specific Vehicle Speed Awareness
  •  Detection of skin sepsis identification with convolutional neural networks
  •  Trash segregation with convolutional neural networks
  •  Weather forecasting through deep learning
  •  AwakeWheel Smart Systems for Proactive Driver Drowsiness Prevention
  •  SecureShield Shielding Public Spaces with Weapon Detection
  •  Deep learning for healthy habits: tv safety distance assurance for kids and adults
  •  Safe rails: deep learning for wildlife detection and train accident prevention
  •  Smart poultry farming: implementing deep learning models for early detection of avian diseases
  •  Deep neural networks for accurate smoke detection in environments with human presence
  •  Safeguarding Crops with Deep Learning Weed Detection
  •  Safe rails: deep learning for wildlife detection and train accident prevention
  •  Enhancing Dermatological Diagnoses: A Deep Learning Approach to Skin Cancer Detection
  •  Guardians of the Wilderness A Smart System for Early Forest Fire Detection
  •  Convolutional neural networks for vehicle zone detection
  •  AccidentAware Tech Harnessing Deep Learning for Proactive Safety Monitoring
  •  UrbanPulse TrafficNext-Gen Technologies for Varied Traffic Intensity Management
  •  UrbanSmart TrafficNext-Gen Technologies for Enhanced Traffic and Parking Management
  •  HelmInsight: Intelligent Insights into Safety Helmet Usage with DL
  •  Warehouse security: deep learning-based fire detection strategies
  •  Intelligent Transportation Systems Deep Learning Lane Line Detection
  •  Revolutionizing Input Methods Hand Gesture Recognition System

TOP 10 Advanced Deep Learning Project Ideas


The World Health Organization (WHO) reported that there are 285 million visually-impaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. Unfortunately, most of these systems are limited in their capabilities. In this paper, we present a comparative survey of the wearable and portable assistive devices for visually-impaired people in order to show the progress in assistive technology for this group of people. Thus, the contribution of this literature survey is to discuss in detail the most significant devices that are presented in the literature to assist this population and highlight the improvements, advantages, disadvantages, and accuracy. Our aim is to address and present most of the issues of these systems to pave the way for other researchers to design devices that ensure safety and independent mobility to visually-impaired people.


In this project, we have a proposed a system where we check if a person is helmet wearing or not using a webcam and if he is not wearing a helmet then face recognition is used for the identification or verification of a person from a digitalized image preferably used in surveillance, security and then we alert the authorities by sending an email as well as sending the image of the person in real-time, for this functionality we are using SMTP library, when fine collected sms send to the individual employees mobile number. Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the industry surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. In this regard, we develop a deep learning-based method for the real-time detection of a safety helmet at the construction site. that is based on convolutional neural networks A dataset containing 2000 images of safety helmets collected from two sources, i.e., manual capture from the video monitoring system at the workplace and open images obtained using web crawler technology, is established and released to the public. The image set is divided into a training set, validation set, and test set. The experiment results demonstrate that the presented deep learning-based model using the faster CNN algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.


Since the development of computer technology, human interaction with computers has evolved. Gestures are an effective method to interact also the era of Covid-19 impacted on us. The mouse as well as the keyboard are devices that interact with computers. Here we have tried to make the mouse and keyboard feature interaction using hand gestures. Eventually discarding the electronic equipment. Hence, controlling the mouse cursor by using your finger and typing on a virtual keyboard. The actions such as clicking, dragging and typing data will be carried out using various hand gestures. The camera's output will be presented on the system's screen so that the user can further calibrate it. We use technologies like Open-CV, Media-Pipe, Python. The Media-Pipe library comes in very handy in AI projects and provides features that help the model’s efficiency. The user will be able to navigate the computer cursor and type using the virtual keyboard with their hand holding color caps or tapes, and left click and dragging will be done using various hand motions. In this paper, we propose a hand gesture recognition system to control the virtual mouse and a virtual keyboard for natural human computer interface.


Vehicle classification and avoidance in hill stations is an important problem that can be solved using computer vision techniques and machine learning algorithms. The proposed system consists of an intelligent camera system that can classify vehicles based on their type and detect potential collisions with other vehicles or obstacles on the road. The system is built using a Convolutional Neural Network (CNN) algorithm for vehicle classification, (RF) encoder and decoder for feature extraction, and an Arduino Uno microcontroller for Alerting the vehicle. In the existing system, vehicle classification and avoidance are typically done manually by the driver, which is not always reliable, especially in hilly terrain. The proposed system overcomes this limitation by automating the process using advanced computer vision and machine learning algorithms. The proposed system uses a camera using vehicle images in the mountain to capture real-time images of the road ahead. The video is then processed using a CNN algorithm for vehicle classification, which can identify various types of vehicles, such as cars, buses. The system uses an RF encoder and decoder to extract features from the images and classify the vehicles accurately. Once a vehicle is classified, the system checks for potential collisions with other vehicles or obstacles on the road. If a potential collision is detected, the system sends a signal to the Arduino Uno microcontroller, which alert the vehicle to avoid the collision.


The illicit felling of trees and the widespread deforestation they cause present serious ecological and environmental problems that need for creative approaches to early detection and prevention. This project abstract describes a novel attempt to identify and stop tree-cutting activities using artificial intelligence (AI). In order to find instances of tree cutting and deforestation, the project uses artificial intelligence (AI) algorithms to scan satellite photos, drone footage, and other pertinent visual data sources. In order to identify patterns and anomalies related to tree removal, such as the presence of chainsaws, tree stumps, and the felling of a significant number of trees quickly, these algorithms use computer vision techniques. To differentiate between legal tree removal such as permitted logging operations—and illicit activity, machine learning algorithms are being developed. These models receive training from a sizable collection of photos with labels that are constantly improved for greater accuracy. Geographic information systems (GIS) are also being used by the initiative to map and monitor hotspots for deforestation, facilitating quick responses and enforcement actions. The project's implementation necessitates the collaboration of diverse stakeholders, ethical considerations, and privacy concerns. The ultimate objective is to use AI as a potent instrument in the continuous endeavors to preserve and safeguard the planet's essential forests.