Knowledge&Technology

What are the Object Detection Algorithms and Libraries?

In this article, we will examine what artificial intelligence object detection is, the best object detection algorithms and libraries for software developers.
Ahmet Faruk Yıldız
3 minutes

What are the Object Detection Algorithms and Libraries?

In recent years, the increase in images, thanks to advances in technology, has led to an increase in unlabeled data in the background. Therefore, drawing meaningful conclusions from this data and analyzing the data has become an important issue for developers. Image processing algorithms are used to solve this problem. In this article, we will review image detection algorithms and libraries for you.

What is AI object detection?

Object detection is a technology that works to identify objects within a photo or video. Object detection technology allows us to identify where an object of interest is located within a given image. When identifying this object, it draws a rectangular bounding box around it. What makes object detection important today is that it helps us understand and analyze scenes in images. 

When looking at object detection, we can differentiate between machine learning and deep learning-based approaches. Machine learning-based approaches use a color histogram technique to identify all groups of pixels belonging to an object. This feature is then fed into a regression model that predicts the location of the object along with its label. On the other hand, deep learning-based approaches, a newer method compared to machine learning, using Convolutional Neural Networks (CNNs) that do not need to identify and extract features separately.

Where is object detection used?

Object detection algorithms are an artificial intelligence technology bonded to computer vision and image processing that detects models of semantic objects of a certain class in images and videos. A concrete example of object detection, which is always with us in our daily lives, is unlocking our phones by face detection. 

The uses of object detection do not stop there. From autonomous vehicles to license plate recognition systems, from object tracking to robotic vision, object detection has become an important part of our lives. Companies investing in these areas especially need software developers working on object detection today.

If we look at the main areas where object detection is used:

  • Object tracking: While watching a game of golf, baseball, or cricket match, the ball may go out of people's field of vision or fall too far away. In such cases, object tracking can provide continuous information about the direction of movement of the ball. 
  • License plate recognition: Both object detection and optical character recognition (OCR) technology are used to identify the characters on a license plate. Object detection is used to capture images from a specific video or photo and detect vehicles, and when the model detects the license plate, OCR attempts to convert the two-dimensional data into machine-encoded text. 
  • Autonomous vehicle technologies: One of the most important elements for autonomous cars is to inspect various elements around the automobile while driving. An object detection model trained on multiple classes to recognize various objects enables autonomous vehicles to perform better. For example, it can tell the difference between a pole and a pedestrian, and many accidents can be avoided.
  • Robotics: Robots are used to lift, carry, pack and pack heavy loads and perform many similar operations. Object detection is required for robots to detect objects and automate them.

What are the object detection algorithms?

After the popularity of deep learning, the quality of algorithms used to solve object detection has steadily increased. Object detection is one of the most challenging topics in computer vision. This challenge stems from object classification and object localization. Several algorithms are available to minimize such challenges. 

Fast R-CNN

Fast R-CNN stands for Fast Region-Based Convolutional Network. Fast R-CNN is an algorithm written in Python and C++. It essentially corrects the disadvantages of R-CNN and SPPnet while improving their speed and accuracy. The advantages of R-CNN are its higher detection quality (mAP) compared to SPPnet and the fact that it does not require a lot of disk storage space to cache features.

Faster R-CNN

Very similar to R-CNN, Faster R-CNN is also an object detection algorithm. Faster R-CNN uses the RPN, which shares full image convolution properties with the detection network at a lower cost than R-CNN and Fast R-CNN. An RPN is essentially a fully convolutional network that simultaneously estimates object boundaries as well as objectivity scores at each location, which is then trained end-to-end to generate high-quality region recommendations that are used by Fast R-CNN.

Spatial Pyramid Pooling (SPP-net)

CNN's require a fixed-size input image and limit the scale to the aspect ratio. For an input image of arbitrary size, CNNs crop the input image to fit the fixed size. However, in some cases, the cropping process causes various regions of objects to be lost. 

CNN consists of two different parts. These are convolutional layers and fully connected layers. Convolutional layers work as sliding windows and do not require a fixed image size. Classification algorithms such as fully connected layers require a fixed-size input. Therefore, the fixed size constraint only comes from fully connected layers.

These types of input vectors can be constructed with a Bag of Words (BoW) approach that aggregates features. Spatial Pyramid Pooling (SPP) enhances BoW by preserving spatial information by pooling in local spatial bins. These bins have sizes proportional to the image size. To use any deep neural network with random-sized images, we simply replace the last pooling layer with a spatial pyramid box layer. This allows us to add images of any aspect ratio and size.

Faster than R-CNN in achieving accuracy, SPP-net computes feature maps from the whole image once and then pools features in random regions to create constant-length representations for the detector. This eliminates the repeated computation of convolutional features.

Histogram of Oriented Gradients (HOG)

HOG, which stands for Histogram of Oriented Gradients, is basically a feature complement for object detection in image processing and computer vision techniques. The descriptive technique of the Histogram of oriented gradients includes, among others, the occurrences of gradient orientation in localized parts of an image, such as the detection window, region of interest (ROI), etc. The advantage of HOG is its simplicity.

Other advantages of HOG: 

  • Creation of a useful feature complement to perform object detection.
  • Ability to be combined with Support Vector Machines (SVM) for high-precision object detection.
  • Creation of a sliding window effect for the calculation of all positions.

Single Shot Detector (SSD)

Single Shot Detector, abbreviated as SSD, is a method for detecting objects in photos or videos using a single deep neural network. 

Advantages of Single Shot Detector (SSD):

  • SSD is an algorithm that encapsulates all computations in a single network, completely eliminating proposal generation and subsequent pixel or feature resampling.
  • Easy to train
  • This algorithm can be easily integrated into systems that require a detection component. 
  • Single Shot Detector is very fast.

You Only Look Once (YOLO)

Used by many software developers, You Only Look Once (YOLO) is one of the popular algorithms for object detection. YOLO's unified architecture is extremely fast. The basic YOLO model processes images simultaneously at 45 frames per second, while a smaller version of this network, Fast YOLO, can process 155 frames per second. You Only Look Once (YOLO) provides better performance than R-CNN.

What are the object detection libraries?

Let's take a look at some important open source custom object detection libraries that you can use in your projects to benefit software developers.

MMDetection

MMDetection is an open-source object detection toolbox based on PyTorch. It can be used to extract, test, and train predefined models with customized datasets.

ImageAI

ImageAI is an easy-to-use Computer Vision Python library that enables developers to easily integrate cutting-edge AI features into existing applications and systems. Designed with simplicity in mind, ImageAI also supports a list of machine learning algorithms for important tasks such as image prediction and object detection. The library currently supports image prediction and training using four separate machine learning algorithms trained on the ImageNet-1000 dataset.

YOLOv3_TensorFlow

YOLO v3 is one of the applications of the YOLO series released in 2018. This new version of YOLO is better than its predecessors in terms of performance, speed, and accuracy. Where it differs from other architectures is that it performs with better precision on smaller objects. The YOLOv3_TensorFlow library is one of the oldest implementations of the YOLO architecture for object detection processing and computation. It includes very fast GPU computations, data pipelines, weight transformations, faster training times, and much more.

Cameralyze's Object Detection Solution

Cameralyze's AI solutions enable fast, easy, and cost-effective object detection with a code-free platform. With its user-friendly interface, high performance, and fast-running web-based system, Cameralyze allows you to easily complete your work and save time. With Cameralyze object detection solutions, you can detect, tag, or track moving objects on photos, videos, or live images. The platform allows you to track and manage your visual data. Register today and get to know the platform by starting your free trial

With Cameralyze, you can perform object detection and take advantage of other AI solutions without having to spend time on libraries or algorithms.

Conclusion

To summarize, in artificial intelligence object detection technology, which has become widespread and popular in recent years, there is confusion due to many different processes. Many algorithms and libraries have been developed to avoid confusion. These algorithms are Fast R-CNN, Faster R-CNN, Histogram of Oriented Gradients (HOG), Single Shot Detector (SSD), and You Only Look Once (YOLO). Object detection libraries that can be used are MMDetection, ImageAI, and YOLOv3_TensorFlow. 

If there is something more practical than all these, it is Cameralyze. Cameralyze is a Computer Vision Platform that offers its users simple and fast solutions without code. In Cameralyze, object detection can be done easily in a short time, thanks to its easy-to-use dashboard.

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