A complete guide to object recognition

Computer vision is currently one of the hottest fields of artificial intelligence, and object recognition plays a key role in its rapid development. This guide will help you understand the basic concepts of object detection. If you are looking for the following questions, you have come to the right place; In this article, we will review all of the following:

What is the difference between object recognition and object identification?

Which computer vision technique should I use for diagnosis?

How should I build an accurate object recognition model?

Stay with us in this article from Avir’s artificial intelligence website.

What is object detection?

Object recognition is a sub-field of computer vision technology that deals with the localization and classification of objects in an image or video.

In simpler terms, object detection is the technology of drawing bounding boxes around detected objects that allow us to place them in a given scene (or how they move in that scene).

Here’s how to detect object with V7. V7 is an artificial intelligence data engine for computer vision and generative artificial intelligence

The difference between object recognition and image classification

Before we move on to object detection, let’s take a look at the difference between object detection and image classification.

Image classification sends an entire image through a classifier (such as a deep neural network) to extract a label from it. Classifiers consider the whole image, but don’t tell you where the label appears in the image.

Object detection is slightly more advanced than image classification because it creates a bounding box around the classified object.

Classification has its own advantages and is a better option for tags that don’t really have physical boundaries, such as “blurry” or “sunny”. However, object detection systems almost always perform better than classification networks in detecting objects that have a physical presence, such as a car.

The difference between object recognition and image segmentation

Image segmentation is the process of determining which pixels of an object class are found in an image.

Semantic image segmentation marks all pixels belonging to that tag, but does not define the boundaries of each object.

Instead, object detection does not segment the object, but clearly marks the location of each individual object instance with a box.

Combining semantic segmentation with object detection leads to instance segmentation, which first identifies object instances and then divides each into identified boxes (known in this case as regions of interest). ).

Advantages and disadvantages of object recognition

Object detection is very good and useful in the following cases:

Detection of objects that occupy between 2 and 60% of the image area.

Detection of objects with clear boundaries.

Identify clusters of objects as 1 item.

Localization of objects at high speed (>15 frames per second)

However, in other scenarios it is outclassed by other methods.

You should always ask yourself: Do these scenarios apply to my problem?

But, here are some tricks you can use when choosing computer vision techniques for your needs.

For objects that are elongated, use sample segmentation!

Long and thin items such as pencils, if identified, occupy less than 10% of the box area. This directs the model to the background pixels instead of the object itself.

Image: A diagonal pencil labeled on V7 using boxes and polygons

For objects that do not have a physical presence, use classification!

Things in an image such as the label “sunny”, “bright” or “crooked” are best identified with image classification techniques – allowing the network to take the image and figure out which features are associated with these labels.

For objects that do not have a clear boundary at different angles, use semantic segmentation!

The sky, ground or vegetation in aerial images do not have clear boundaries. Semantic segmentation is more efficient in “painting” pixels that belong to these classes. Object detection still takes the “sky” as an object, but still has trouble with such objects.

For objects that are commonly blocked, use instance partitioning if possible!

Blockage is handled much better in two-shot detection networks than in single-shot methods. In this branch of detectors, sample segmentation models do a better job in understanding and segmenting occluded objects than bounding box detectors.

Types and modes of object recognition

Before deep learning started in 2013, almost all object recognition work was done through classical machine learning techniques. Common ones included Viola-Jones object detection technique, scale-invariant feature transforms (SIFT) and histogram of oriented gradients.

These identify a number of common features across the image and classify their clusters using logistic regression, color histograms, or random forests. Today’s techniques based on deep learning are much better than these techniques.

Approaches based on deep learning from neural network architectures such as RetinaNet, YOLO, CenterNet, SSD or Single Shot Multibox detector, region proposals (R-CNN, Fast -RCNN, Faster RCNN, Cascade R-CNN) for the ability to detect objects, and then identify labels.

How object recognition technology works

Object recognition is generally divided into 2 stages:

Single stage object detectors.

Two-stage object detectors.

The latest object recognition architectures include 2-stage architectures, many of which are pre-trained on the COCO dataset. COCO is an image dataset consisting of 90 different classes of objects (cars, people, sports balls, bicycles, dogs, cats, horses, etc.).

The dataset was collected to solve common object detection problems. Today, this image is becoming obsolete as its images were mostly taken in the early 2000s, making them much smaller, grainier, and with different objects than today’s images. Newer datasets such as OpenImages are taking their place as practical pre-training datasets.

Single stage object detectors

A one-step detector eliminates the RoI extraction process and directly classifies and then regresses the candidate anchor boxes. For example YOLO family (YOLOv2, YOLOv3, YOLOv4, YOLOv5), CornerNet, CenterNet and others. For example, let’s take a look at how YOLO works.

Yulo

YOLO is an object recognition architecture that stands for YOU ONLY LOOK ONCE. YOLO involves using a single neural network trained end-to-end to take images as input and predict bounding boxes and class labels for each bounding box directly. YOLO is a typical single stage detector.

Two-stage object detectors

Two-stage detectors divide the task of object detection into two stages: they extract RoIs (region of interest), then classify the RoIs, and then regression and regression occur. Examples of object detection architectures that are two-stage oriented include R-CNN, Fast-RCNN, Faster-RCNN, Mask-RCNN, and others. For example, let’s take a look at Mask R-CNN.

Mask R-CNN

Mask R-CNN is a typical sample segmentation technique for object detection. This architecture parallels the existing branch for classification and bounding box regression by adding a branch to predict segmentation masks per RoI, and extends R-CNN faster. The mask branch is a small FCN that is applied to each RoI and predicts a segmentation mask on a pixel-by-pixel basis. Below is an architectural representation of Mask R-CNN.

On the other hand, Faster R-CNN is an object recognition model that uses a region proposal network or RPN with feature maps generated from the convolution layer to estimate ROI pooling on Faster. R-CNN improves.

Below is the architecture diagram of Faster R-CNN.

Furthermore, Fast R-CNN is an improved version of R-CNN that gathers CNN features independently of their region of interest (ROI) in one forward pass over the image. In general, R-CNN (region selection with CNN features) is slow because it performs a forward ConvNet for each proposed scheme, without joint computation.

Hence, Fast R-CNN was developed to solve the problem of slow computation.

Important applications of object recognition

Finally, let’s take a look at some of the most common use cases for object detection technology.

1. Recognition of faces and persons

Most facial recognition systems work with object recognition technology. Object recognition technology can be used to recognize faces, classify emotions or expressions, and use the information obtained from boxes in an image retrieval system to identify a specific person from a group.

Face recognition is one of the most popular uses for object recognition, and you use it every time you unlock your phone with your face.

People detection is commonly used to count the number of people in retail stores or to ensure social distancing measures.

2. Intelligent video analysis

Object detection in intelligent video analytics (IVA) is used wherever there are CCTV cameras in retail locations to help shopkeepers understand how shoppers interact with products. These video streams pass through an anonymous pipeline to blur people’s faces and de-identify them. Some uses of IVA preserve privacy by only looking at people’s shoes, by placing cameras below the knee and ensuring that the system captures a person’s presence without looking directly at their identifiable features. IVA is commonly used in factories, airports and transportation centers to track queue length and access to restricted areas.

3. Autonomous vehicles

Self-driving cars use object recognition to detect pedestrians, other vehicles, and obstacles on the road in order to navigate around safely. Self-driving vehicles equipped with LIDAR sensors sometimes use 3D object detection, which applies cubes around objects.

4. Smart image surgery

Surgical film is very noisy data taken from endoscopes during critical operations. Object detection can be used to detect things that are difficult to see, such as polyps or lesions that require immediate attention from a surgeon. This technology is also used to inform the hospital staff about the operation status.

5. Defect and failure inspection

Manufacturing companies can use object detection to detect defects in the production line. Neural networks can be trained to detect small defects, from folds in fabric to dents in a product.

Unlike traditional machine learning approaches, object recognition based on deep learning can also detect defects in very different objects, such as food.

6. Pedestrian detection

This recognition is one of the most essential tasks of computer vision, which is used in robotics, video surveillance, and vehicle safety. Pedestrian detection plays a key role in object recognition research because it provides essential information for the semantic understanding of video footage.

However, despite its relatively high efficiency, this technology still faces challenges such as different clothing styles in appearance or the presence of obstructions that reduce the accuracy of existing detectors.

7. Artificial intelligence drone navigation

Drones today use incredible cameras and can use cloud-hosted models to assess any object they come across.

For example, they can be used as an alternative to helicopters to inspect hard-to-reach areas on bridges for cracks, fractures, and other structural damage, or to inspect power lines.

And in the end that…

In this article, we reviewed the object detection technology and its applications. We at Avir Artificial Intelligence Company are by your side to introduce the latest and most up-to-date technologies based on artificial intelligence. If you intend to buy artificial intelligence products and software, contact Avir right now from the contact section or through the contact number 88667157-(021)!