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Introduction
Object detection, in simple terms, is a method that is used to recognize and detect different objects present in an image or video and label them to classify these objects. Object detection typically uses different algorithms to perform this recognition and localization of objects, and these algorithms utilize deep learning to generate meaningful results.
Deep learning object detection is a fast and effective way to predict an object’s location in an image, which can be helpful in many situations. RCNN or Region-based Convolutional Neural Networks, is one of the pioneering approaches that is utilised in object detection using deep learning.
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Object Detection
Object detection technique helps in the recognition, detection, and localization of multiple visual instances of objects in an image or a video. It provides a much better understanding of the object as a whole, rather than just basic object classification. This method can be used to count the number of instances of unique objects and mark their precise locations, along with labeling. With time, the performance of this process has also improved significantly, helping us with real-time use cases. All in all, it answers the question: “What object is where and how much of it is there?”.
What is an Object?
An object is an element that can be represented visually. The physical characteristics of an object do not have a wide range of variability. An object must be semi-rigid to be detected and differentiated.
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History of Object Detection
In the last 20 years, the progress of object detection has generally gone through two significant development periods, starting from the early 2000s:
1. Traditional object detection- the early 2000s to 2014.
2. Deep learning-based detection- after 2014.
The technical evolution of object detection started in the early 2000s and the detectors at that time. They followed the low-level and mid-level vision and followed the method of ‘recognition-by-components’. This method enabled object detection as a measurement of similarity between the object components, shapes, and contours, and the features that were taken into consideration were distance transforms, shape contexts, and edgeless, etc. Things did not go well and then machine detection methods started to come into the picture to solve this problem.
Multi-scale detection of objects was to be done by taking those objects into consideration that had “different sizes” and “different aspect ratios”. This was one of the main technical challenges in object detection in the early phases. But, after 2014, with the increase in technical advancements, the problem was solved. This brought us to the second phase of object detection, where the tasks were accomplished using deep learning.
Concept
The main concept behind this process is that every object will have its features. These features can help us to segregate objects from the other ones. Object detection methodology uses these features to classify the objects. The same concept is used for things like face detection, fingerprint detection, etc.
Let us take an example, if we have two cars on the road, using the object detection algorithm, we can classify and label them.
Definition
Object detection is a process of finding all the possible instances of real-world objects, such as human faces, flowers, cars, etc. in images or videos, in real-time with utmost accuracy. The object detection technique uses derived features and learning algorithms to recognize all the occurrences of an object category. The real-world applications of object detection are image retrieval, security and surveillance, advanced driver assistance systems, also known as ADAS, and many others.
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General description of Object Detection
We humans can detect various objects present in front of us and we also can identify all of them with accuracy. It is very easy for us to count and identify multiple objects without any effort. Recent developments in technologies have resulted in the availability of large amounts of data to train efficient algorithms, to make computers do the same task of classification and detection.
There are so many terms related to object recognition like computer vision, object localization, object classification, etc. and it might overwhelm you as a beginner, so let us know all these terms and their definitions step by step:
- Computer Vision: It is a field of artificial intelligence that enables us to train the computers to understand and interpret the visuals of images and videos using algorithms and models.
- Image Classification: It involves the detection and labeling of images using artificial intelligence. These images are classified using the features given by the users.
- Object Localization: It involves the detection of different objects in a given visual and draws a boundary around them, mostly a box, to classify them.
- Object Detection: It involves both of these processes and classifies the objects, then draws boundaries for each object and labels them according to their features.
All of these features constitute the object recognition process.
How does Object Detection work?
Now that we have gone through object detection and gained knowledge on what it is, now it’s the time to know how it works, and what makes it work. We can have a variety of approaches, but there are two main approaches- a machine learning approach and a deep learning approach. Both of these approaches are capable of learning and identifying the objects, but the execution is very different.
Also Read: TensorFlow Object detection Tutorial
Methods for Object Detection
Object detection can be done by a machine learning approach and a deep learning approach. The machine learning approach requires the features to be defined by using various methods and then using any technique such as Support Vector Machines (SVMs) to do the classification. Whereas, the deep learning approach makes it possible to do the whole detection process without explicitly defining the features to do the classification. The deep learning approach is majorly based on Convolutional Neural Networks (CNNs).
Machine Learning Methods
- Scale-Invariant Feature Transform (SIFT)
It is counted amongst the most involved algorithms as it performs four major tasks: scale-space peak selection, orientation assignment, key point description and key point localization. Some of the major advantages of using this algorithm include locality, detailed distinctiveness, real-time performance, the ability to extend to a wide range of different features and robustness.
2. Histogram of Oriented Gradients (HOG) features
It is a feature descriptor similar to Canny Edge Detector and SIFT. This uses the technique of counting occurrences of gradient orientation in a localized portion of the image. This descriptor mainly focuses on the shape of an object. It is better than most edge descriptors as it takes the help of the magnitude and the gradient angle to assess the object’s features. It then produces a histogram for the region it assessed using the magnitude and orientations of the gradient.
3. Viola-Jones object detection framework
This object detection framework works best in the case of detecting human faces. This object detection framework combines the best of Haar-like features, Integral Images, the AdaBoost Algorithm and the Cascade Classifier in order to curate a system that is best in class for object detection and is highly accurate.
Deep Learning Methods
- Region Proposals (R-CNN, Fast R-CNN, Faster R-CNN) In this method, the region proposal layer outputs bounding boxes around the objects of the image as a part of the region proposal network. The image gets divided under this process into some superpixels and then combined adjacent to the region.
- You Only Look Once (YOLO) This algorithm works in real-time and helps recognise various objects in a picture. This algorithm uses a regression method, which helps provide class probabilities of the subjected image. It works by devoting the image into N grids with an equal dimensional region of SxS.
- Deformable convolutional networks This method of mathematical operations allows the merging of two sets of information. With DCN, 2D offsets are added into the regular grid sampling locations into the standard convolution. This helps create free-form deformation of the sampling grid. This network filter is also known as a kernel or future detector.
- Refinement Neural Network for Object Detection (RefineDet) This model is equally famous in object detection using deep learning and is often used as an alternative to YOLO, SSD and CNN models. This model generates a predetermined number of bounding boxes and scores that indicate the existence of the unique kinds of items in the boxes. Two major components of this model are the object detection module (ODM) and the anchor refinement module (ARM).
- Retina-Net This object detection model is chosen to be the best-performing one, particularly in the case of dense and small-scale objects. That is why it is mainly used in aerial and satellite imagery. It is a one-stage object detection model which takes the help of a focal loss function to address the class imbalance while training.
We shall learn about the deep learning methods in detail, but first, let us know what is machine learning, what is deep learning, and what is the difference between them.
What is Machine Learning?
Machine learning is the application of Artificial Intelligence for making computers learn from the data given to it and then make decisions on their own similar to humans. It gives computers the ability to learn and make predictions based on the data and information that is fed to it and also through real-world interactions and observations. Machine learning, basically, is the process of using algorithms to analyze data and then learn from it to make predictions and determine things based on the given data.
Machine learning algorithms can take decisions on themselves without being explicitly programmed for it. These algorithms make mathematical models based on the given data, known as a ‘training set’, to make the predictions. In machine learning algorithms, we need to provide the features to the system, to make them do the learning based on the given features, this process is called Feature Engineering.
The day to day examples of machine learning applications is voice assistants, email-spam filtering, product recommendations, etc.
How object detection using machine learning is done?
Object detection using machine learning is supervised in nature. Supervised learning is a machine learning process that utilises prelabelled training data and based on those datasets the machine tries to predict the outcomes of the given problem. Apart from object detection. Supervised learning can also be used in image classification, risk assessment, spam filtering etc.
Now in the case of object detection deep learning, the area of application can greatly differ. The reason is image classification can only assess whether or not a particular object is present in the image but fails to tell its location of it. Whereas deep learning object detection can do all of it, as it uses convolution layers to detect visual features.
Along with object detection deep learning, the dataset used for the supervised machine learning problem is always accompanied by a file that includes boundaries and classes of its objects.
What is Deep Learning?
Deep learning, which is also sometimes called deep structured learning, is a class of machine learning algorithms. Deep learning uses a multi-layer approach to extract high-level features from the data that is provided to it. It doesn’t require the features to be provided manually for classification, instead, it tries to transform its data into an abstract representation. It simply learns by examples and uses it for future classification. Deep learning is influenced by the artificial neural networks (ANN) present in our brains.
Most of the deep learning methods implement neural networks to achieve the results. All the deep learning models require huge computation powers and large volumes of labeled data to learn the features directly from the data. The day to day applications of deep learning is news aggregation or fraud news detection, visual recognition, natural language processing, etc.
Object Detection using Deep Learning
Now that we know about object detection and deep learning very well, we should know how we can perform object detection using deep learning.
These are the most used deep learning models for object detection:
1. R-CNN model family: It stands for Region-based Convolutional Neural Networks
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- R-CNN
- Fast R-CNN
- Faster R-CNN
2. YOLO model family: It stands for You Look Only Once
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- YOLOv1
- YOLOv2 and YOLOv3
Let us look at them one by one and understand how they work.
The object detection process involves these steps to be followed:
- Taking the visual as an input, either by an image or a video.
- Divide the input visual into sections, or regions.
- Take each section individually, and work on it as a single image
- Passing these images into our Convolutional Neural Network (CNN) to classify them into possible classes.
- After the classification, we can combine all the images and generate the original input image, but also with the detected objects and their labels.
Region-based Convolutional Neural Networks (R-CNN) Family
There are several object detection models under the R-CNN Family. These detection models are based on the region proposal structures. These features have made great development with time, increasing accuracy and efficiency.
The different models under R-CNN are:
- R-CNN
The R-CNN method uses a process called selective search to find out the objects from the image. This algorithm generates a large number of regions and collectively works on them. These collections of regions are checked for having objects if they contain any object. The success of this method depends on the accuracy of the classification of objects.
- Fast-RCNN
The Fast-RCNN method uses the structure of R-CNN along with the SPP-net (Spatial Pyramid Pooling) to make the slow R-CNN model faster. The Fast-RCNN uses the SPP-net to calculate the CNN representation for the whole image only once. It then uses this representation to calculate the CNN representation for each patch generated by the selective search approach of R-CNN. The Fast-RCNN makes the process train from end-to-end.
The Fast-RCNN model also includes the bounding box regression along with the training process. This makes both the processes of localization and classification in a single process, making the process faster.
- Faster-RCNN
The Faster-RCNN method is even faster than the Fast-RCNN. The Fast-RCNN was fast but the process of selective search and this process is replaced in Faster-RCNN by implementing RPN (Region Proposal Network). The RPN makes the process of selection faster by implementing a small convolutional network, which in turn, generates regions of interest. Along with RPN, this method also uses Anchor Boxes to handle the multiple aspect ratios and scale of objects. Faster-RCNN is one of the most accurate and efficient object detection algorithms.
| R-CNN | Fast-RCNN | Faster-RCNN | |
| Test time per image | 50 seconds | 2 seconds | 0.2 seconds |
| Speed | 1x | 25x | 250x |
You Look Only Once (YOLO) Family
The R-CNN approach that we saw above focuses on the division of a visual into parts and focus on the parts that have a higher probability of containing an object, whereas the YOLO framework focuses on the entire image as a whole and predicts the bounding boxes, then calculates its class probabilities to label the boxes. The family of YOLO frameworks is very fast object detectors.
The different models of YOLO are discussed below:
- YOLOv1
This model is also called the YOLO unified, for the reason that this model unifies the object detection and the classification model together as a single detection network. This was the first attempt to create a network that detects real-time objects very fast. YOLO only predicts a limited number of bounding boxes to achieve this goal.
- YOLOv2 and v3
YOLOv2 and YOLOv3 are the enhanced versions of the YOLOv1 framework. YOLOv2 is also called YOLO9000. The YOLOv1 framework makes several localization errors, and YOLOv2 improves this by focusing on the recall and the localization. The YOLOv2 uses batch normalization, anchor boxes, high-resolution classifiers, fine-grained features, multi-level classifiers, and Darknet19. All these features make v2 better than v1. The Darknet19 feature extractor contains 19 convolutional layers, 5 max-pooling layers, and a softmax layer for the classification of objects that are present in the image.
The YOLOv3 method is the fastest and most accurate object detection method. It accurately classifies the objects by using logistic classifiers compared to the softmax approach used by YOLOv2. This makes us capable of making multi-label classifications. The YOLOv3 also uses Darknet53 as a feature extractor, which has 53 convolutional layers, more than the Darknet19 used by v2, and this makes it more accurate. It also uses a small object detector to detect all the small objects present in the image, which couldn’t be detected by using v1.
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Summary
I hope the above overview of object detection and its implementation using deep learning was helpful to you and made you understand the core idea of object detection and how it is implemented in the real-world using various methods and specifically using deep learning.
Object detection can be used in many areas to reduce human efforts and increase the efficiency of processes in various fields. Object detection, as well as deep learning, are areas that will be blooming in the future and making its presence across numerous fields. There is a lot of scope in these fields and also many opportunities for improvements.
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