Object Detection
The Object Detection section deals with the analysis of images for the purpose of recognizing objects of interest within them. This is done using an artificial intelligence model which must be trained to recognize various elements.
What is Object Detection?
Object Detection is an artificial intelligence technology that allows computers to identify and locate specific objects in an image or video. These objects can be anything, such as people, animals, cars, household items, etc. In the industrial field, this tool is used to improve the automation of production processes, safety, and product quality. Some examples of use are listed below:
Quality Control: Object Detection can be used to inspect products on a production line and detect defects or anomalies. For example, it can be used to identify scratches, cracks, stains, or other imperfections on objects such as cars, electronics, food products, etc.
Tracking and Classification: in the industry, it is often necessary to monitor the movement and position of objects. Object detection can be used to track and classify objects in real-time, for example, to keep track of the flow of products on an assembly line or to manage warehouse logistics.
Safety: Object Detection can help improve workplace safety by identifying the presence of dangerous people or objects in restricted areas. For example, it can be used to detect the improper positioning of equipment.
Logistics and Packaging: in logistics and packaging, Object Detection can be used to detect and correctly position objects on conveyor belts, pallets, or transport vehicles, improving efficiency and reducing errors.
Equipment Monitoring: Object detection can be applied for monitoring and predictive maintenance of industrial equipment. The technology can identify signs of wear or damage to machinery, preventing unforeseen failures.
How is Object Detection performed?
Object Detection is carried out using deep learning models, specifically convolutional neural networks (CNNs). Here is an overview of the process:
Data Preparation: Before starting, it is necessary to collect a large dataset containing images or videos with labeled objects of interest. These labels indicate the presence and location of objects in the image.
Model Training: An Object Detection model is trained on this dataset. During training, the model learns to recognize patterns and distinctive features of objects so that it can identify and locate them.
Inference: Once trained, the model can be used to perform Object Detection on new images or videos. The model analyzes the input and returns the positions and classes of the detected objects.
What is meant by training a model and how is it done?
The training of an Object Detection model is the process by which the model learns to recognize objects of interest. During training, the model adjusts its internal parameters to minimize the error between its predictions and the labels provided in the training data.
A fundamental element for training is the use of a good training set, which is a set of images or videos with objects manually labeled by the user. Each label or tag indicates the class of the object and its position in the image. The model learns from the data of this set to be able to recognize objects in new images. A representative and high-quality training set is therefore essential to achieve good results in terms of recognition accuracy.
The sections that make up the Object Detection module allow both to display images containing highlighted elements of interest and to train the model with the desired parameters.
