TEAM SIGMA

Image detection using AI algorithms Implementing MQTT protocol or secure cloud comm protocol

2. Improve the Car

Implementing MQTT

Object detection

Problem

index

Implementing MQTT protocol.

Image detection using AI Algorithms.

IMPROVE THE CAR

PROBLEM

Image Recognition is the task of identifying objects of interest within an image and recognizing which category the image belongs to. Image recognition, photo recognition, and picture recognition are terms that are used interchangeably. Image recognition with artificial intelligence is a long-standing research problem in the computer vision field. While different methods to imitate human vision evolved over time, the common goal of image recognition is the classification of detected objects into different categories . Therefore, it is also called object recognition. In past years, machine learning, in particular deep learning technology, has achieved big successes in many computer vision and image understanding tasks. Hence, deep learning image recognition methods achieve the best results in terms of performance and flexibility. Later in this article, we will cover the best-performing deep learning algorithms and AI models for image recognition.

Image detection using AI algorithms

PROBLEM

A convolutional neural network (CNN or ConvNet), is a network architecture for deep learning which learns directly from data, eliminating the need for manual feature extraction. CNNs are particularly useful for finding patterns in images to recognize objects, faces, and scenes. They can also be quite effective for classifying non-image data such as audio, time series, and signal data. Applications that call for object recognition and computer vision — such as self-driving vehicles and face-recognition applications — rely heavily on CNNs.

solution: CNN

Classification Layers

The final layer of the CNN architecture uses a classification layer such as softmax to provide the classification output.

Shared Weights and Biases

Like a traditional neural network, a CNN has neurons with weights and biases. The model learns these values during the training process, and it continuously updates them with each new training example. However, in the case of CNNs, the weights and bias values are the same for all hidden neurons in a given layer.

Feature Learning, Layers, and Classification

ConvolutionRectified linear unit (ReLU)Pooling

How CNNs Work

• Connectivity is often unreliable as a car can move through network blind-spots. The process of reconnecting with the cloud can result in slower response time and lost messages
• Network latency can become an issue again due to cellular network performance. For a responsive user experience, the car must be able to deal with network latency.
• The cloud platform needs to be able to scale up and down to support millions of vehicles that connect at various points of time
• A connected vehicle needs to operate in a trusted environment so hackers can’t take control of the car.

• Bi-directional messaging

Besides the bi-directional messaging challenge, there are a number of other unique technical challenges for connected car services:

Why MQTT Is Essential for Building Connected Cars

PROBLEM