What are some innovative computer vision projects to include in your AI portfolio?

So, there's this cool project I did where I used computer vision to help doctors with medical images. It was super accurate for over five months, like 100%! What made it even cooler was using UiPath, this automation tool, to make everything run smoothly. It showed me how combining computer vision with automation can make a big difference in healthcare. I'm excited to share this experience in my AI portfolio and explore more projects that bring together technology to solve real problems.

The face mask detection project is technically challenging but socially impactful. Key skills one can demonstrate in such a project are: 1. Image Processing: Using computer vision libraries like OpenCV to process and analyze images or video streams. 2. Model Training: Training AI models to recognize faces and determine mask presence. 3. Transfer Learning: Utilizing pre-trained models and fine-tuning them on a specific dataset. Popular models for this project are MobileNetV2 and ResNet50, known for their efficiency in image classification tasks. In addition, models like YOLO, SDD, VGG16 and InceptionV3 can also be considered.

With the advent of technologies in Facial Recognition solutions, the maximum usage of this technology has surpassed the limitation of just identifying a person! It has become more important to keep a track of any possible security breaches or similar incidents that are hideous ever since COVID 19 arrived! My vision exhibits an idea of ensuring safety by unveiling potential ‘black sheep’ in a normal crowd via Face Mask detection!!

To make this work first apply a CNN to capture the feature in the image then utilize LSTMs that are basically a type of RNNs to generate the captions. It has multiple use cases for example it comes in handy in web development.

This type of project is excellent because it is adding complexity. Two types of neural networks are combined, which are CNNs and RNNs. It is advised that you should understand how both architectures work, in this way, you will be able to build a project for Image Captioning. Also, something important to mention is that when extracting image features there are two main approaches to pass these features. The first is by initializing the hidden state of the RNN and the other is by using the features as input at each time step. I recommend the first one of initializing the hidden state of the RNN, this helps to give context and generate better predictions (text sequences).

Now, let me share another captivating chapter from my computer vision journey – the enchanting realm of image captioning. Picture this: a system that not only sees an image but also weaves a tale about it. Imagine a deep learning adventure where a convolutional neural network (CNN) unravels the image's secrets, and a recurrent neural network (RNN) crafts a narrative that brings it to life. My journey led me to datasets like Flickr8k and MSCOCO, where each image became a canvas for my model's creativity.

Image captioning!!! And the ‘must have’, minute precision capabilities embedded in medical grade devices in an affordable and accessible manner to simplify complexities and detection of various upcoming diseases in coming 20 years!!

It is an artistic and innovative computer vision project where one can blend the style of one image with the content of another. Using neural style transfer algorithm. One can manipulate images to reflect the artistic style of famous paintings or particular aesthetics. For example, imagine transforming a regular photo to have the brushstroke style of Van Gogh's "Starry Night". With this project one can not only demonstrate skills in image processing and neural networks but also in creatively applying AI, offering a blend of art and technology.

This reminds me of an image to image translation model “CycleGAN”. I would consider it as a style transfer and what’s cool is the style can be transferred back and forth! Go give it a try :)

In my AI portfolio, I demonstrated expertise in real-time object detection using NVIDIA's GPU acceleration with the YOLO algorithm. I incorporated multiple-stage detection techniques, especially in projects like object tracking, to enhance precision in complex scenarios. Additionally, I leveraged NVIDIA's hardware acceleration in conjunction with NVIDIA TAO (Train, Adapt, Optimize) to fine-tune models for specific tasks, such as optimizing multi-stage object detection for autonomous vehicles. The projects showcase my commitment to staying at the forefront of technology, utilizing both NVIDIA's powerful hardware and TAO for improved performance and accuracy in computer vision applications.

If you perform object detection in a video, you might need to take temporal information into account. You are now performing object tracking. You can identify key points of your object and compute the temporal trajectories using Kalman filter which takes physics rules into account.

Object detection at the edge (IoT to perform edge inference) - Deploy I eject detection model at the edge to perform inference and send data back to cloud for analysis and evaluación (we use the data collected to retrain the model and deploy a new endpoints)

Este desarrollo está siendo muy útil en la industria manufacturera. Para el paso de distintas etapas dentro del proceso productivo, se verifica a través del análisis de imagen, si el producto cumple con las especificaciones requeridas (producto, embalaje, ubicación en centro de distribución y despacho a clientes). Incluso se puede extender a las instalaciones del cliente para verificar la entrega y el estado en la recepción del producto.

Now, let me take you through a chapter of my computer vision journey that felt like straight out of a superhero story – the incredible feat of using the YOLO (You Only Look Once) model for object detection. Picture this: the challenge was limiting the number of people entering a store during the COVID period. Armed with YOLOv5, a cutting-edge object detection model, I trained it to identify the number of people standing in the store. The magic happened when bounding boxes gracefully enveloped each person in the frame, a digital superhero cape ensuring safety. This project, became a real-life superhero ally in managing store capacity during the pandemic.

In this field, there are multiple applications, such as in air security. However, extreme care must be taken with images of disturbances and clothing that cannot be recognized by facial artificial intelligence due to their composition and design, which can result in a sock being registered as an elephant, among other things.

Once I created a Face recognition model using Keras Python libraries and Tensorflow. You can use a grey scale data set or a real set of color images as you like. Here I used a grey scale data set and used CNN with 4 convolutional layers. And the data set had 35,000 Grayscale images. I have been able to get 92% accuracy from the model.

El reconocimiento facial pasa a ser un must hoy en día. Un tremendo desafío frente a temas de cyber seguridad hoy en día respecto a los cambios tecnológicos

Muy de acuerdo por la posibilidad de utilizar dicha información de muchas maneras. He visto como ha ido mejorando la performance en el tiempo y como se ha masificado su uso.

Now, let me share a chapter where simplicity met effectiveness in my computer vision journey – the tale of face recognition for a banking project. Picture this: armed with Python's CV2 library, I seamlessly implemented face recognition, finding no need to delve into more complex machine learning models. The ease and accuracy of CV2 proved to be the perfect fit for the banking project's requirements, effortlessly verifying identities and enhancing security. The simplicity of the solution resonated with the project's needs, and sometimes, simplicity is the ultimate sophistication.

Various applications exist within the realm of point-of-sale systems and supply chain management, particularly in demand planning processes. They also extend to inventory management and warehouse administration for companies dealing with high SKU turnovers, such as those in the fast-moving consumer goods sector and in storage and distribution services

Person re-identification is an interesting project. This involves recognizing persons across multiple camera views or frames. This is challenging because of the change in lighting, pose, accessories, clothing, occlusions etc. This task can be done with Siamese neural networks trained with triplet loss (training network on triplets of image-anchor, positive and negative). You will need to incorporate spatio-temporal features during feature extraction. Datasets like Market-1501, DukeMTMC-reID, CUHK03 can be used for this task.

Numerous intriguing challenges in reinforcement learning leverage pixel or image-based observation spaces. Despite the inherent distinction between reinforcement learning and supervised learning, many advancements in computer vision can enhance the efficiency and effectiveness of self-learning task completion. I recommend applying your acquired computer vision knowledge to this domain, offering fresh perspectives on the capabilities and limitations of your preferred models or methods from supervised learning.

The integration of computer vision with satellite data stands as a pivotal advancement in diverse sectors, revolutionizing biodiversity monitoring, logistics route management, and critical infrastructure monitoring. In biodiversity, it enables precise tracking of ecosystem changes and wildlife movements, aiding in conservation efforts. For logistics, this technology transforms route planning by analyzing real-time terrain and traffic conditions, optimizing delivery routes for efficiency. In infrastructure monitoring, it serves as a proactive tool, detecting structural changes and potential hazards in critical facilities like bridges and power lines, thereby preventing disasters and ensuring safety.

Object detection in conditions such as lowlights, night, early mornings in fog, snow, sandstorms etc which are visually challenging is what we in our team have taken some challenge to work on. Essentially distortion capacity of precision in identifying correctness in prediction is exciting!