Introduction to object detection with deep learning

The application of object detection has penetrated into our daily life, including security, autonomous vehicle systems, etc. Object detection models input visuals (images or videos) and output labeled versions around each corresponding object. This is easier said than done, as object detection models need to take into account complex algorithms and datasets that have been perfected and developed as we speak.

Here's what we're going to cover today, to give you a comprehensive introduction to object detection:

1. The basis of target detection

Before diving into object detection applications, use cases, and basic object detection methods, it is crucial to have a solid understanding of object detection itself. The term is often used interchangeably with techniques such as image classification, object recognition, segmentation, etc. However, it must be admitted that many of the tasks mentioned above are separate tasks, usually belonging to object detection. It is inaccurate to use them equated with each other because they both involve equally important tasks.

What is object detection

Object detection is a deep computer vision technique that focuses on recognizing and labeling objects in images, videos, and even live footage. To perform this process on new data, an object detection model is trained using the remaining annotated visual images. It becomes as easy as inputting visuals and receiving fully labeled output visuals. We will discuss object detection models in more depth later. A key component is object detection bounding boxes, which identify the edges of objects marked with sharp quadrilaterals—usually squares or rectangles. They are all accompanied by the label of the object, whether it is a person, car or dog to describe the target object. Bounding boxes can overlap to reveal multiple objects in a given shot, as long as the model has prior knowledge of the items it labels.

Object detection and other tasks

Let's break down the other computer vision tasks individually to better understand each one:

• Image classification : This is the prediction of the category of items in an image. For example, when you perform a reverse image search on Google, you might get a prompt that says "May contain 'x', where 'x' is the main object of the image detected by this technique. Image classification can show that images are A specific object, but it refers to a main object and does not provide the location of the object in the vision.
• Segmentation : Also known as semantic segmentation, it is the task of grouping together pixels with comparable attributes, rather than identifying objects with bounding boxes.
• Object localization : The difference from object detection is subtle but obvious. Object localization aims to identify the location of one or more objects in an image, while object detection identifies all objects and their boundaries with less focus on location.

2. Deep Learning vs Machine Learning

Now that you have our basic introduction to object detection, it's time to look at the two main models for object detection: deep learning and machine learning. Data analysts often consider deep learning methods to be relatively advanced because it is considered more intuitive and does not require much human intervention. Ultimately, both methods will produce accurate results, but this time we will focus on object detection with deep learning.

What is object detection with deep learning?

What separates object detection with deep learning from other methods is the use of convolutional neural networks (CNN). Neural networks mimic the complex neural structure of the human brain. They mainly consist of input layer, hidden inner layer and output layer. The learning of these neural networks can be supervised, semi-supervised and unsupervised, referring to how much training data is annotated, if any (unsupervised). Because CNNs are able to learn automatically with less human engineering, deep neural networks for object detection yield the fastest and most accurate single and multiple object detection results to date. There is a world of deep learning and cnn to unravel, but today we only focus on key points about object detection algorithms and models.

3. Methods and Algorithms

Object detection is impossible without a model specifically designed to handle this task. These object detection models are trained with tens of thousands of visual content to optimize detection accuracy on an automatic basis later. Models can be trained and refined efficiently with the help of readily available datasets such as COCO (Common Objects in Context), helping you get a head start in scaling your annotation pipeline.

Let's take a closer look at several of the most prominent object detection algorithms and approaches.

R-CNN, Fast R-CNN, Faster R-CNN

The first family of largely successful methods was R-CNN (Region-based Convolutional Neural Networks), which was proposed in 2014. It surpasses previous methods by only extracting 2000 regions from an image, which are called region proposals, instead of the large number of regions previously. The flowchart of R-CNN is as follows: an input image is selected from which 2000 region proposals are extracted. Next, features are extracted from each individual region, which is then classified into one of the known classes. The main disadvantage of R-CNN is that although 2000 region proposals are extracted, the process is very long. This is what paved the way for the new and improved Fast R-CNN.

Not only the object detection process with a large number of regions is time-consuming, but also the training of CNNs with so many regions is time-consuming. Fast R-CNN greatly reduces processing time by feeding images into a pretrained CNN to generate convolutional feature maps, eliminating the process of decomposing images into 2000 region proposals. Instead, region proposals can be easily identified from feature maps, sending them to the RoI pooling layer, which extracts features from a given region. The output of the previous layer is then processed by a fully connected layer, where the model is split into two outputs: one for class prediction via a softmax layer and another for bounding box prediction via a linear output.

How important is the jump from R-CNN to Fast R-CNN? CNN training time dropped from 84 hours to 9 hours . Also, the test time dropped from 50 seconds to 2.5 seconds .

A third, more upgraded model was later introduced, dubbed Faster R-CNN. The architecture is similar to Fast R-CNN, with some notable tweaks. Faster R-CNN does not use selective search, which is based on hierarchical grouping of similar regions. The Regional Proposal Network will take its place in order to finalize regional proposals in record time. It reduces the 2.5-second test speed from Fast R-CNN to an unbeatable 0.2 seconds , making it the fastest of its predecessors and the best choice for real-time object detection.

YOLO

What if we say there is a convolutional neural network that is faster than R-CNN? Well, there is! In 2015, a family of neural networks was proposed, abbreviated as YOLO, in reference to the famous phrase "You only live once". This relies on the simple fact that the network "looks" or passes through the network only once before outputting the final image. This allows object detection with real-time footage, which is quite desirable for surveillance-related applications. Due to its exceptional speed, the accuracy of detected objects is lower than the aforementioned models, but it still manages to be a top contender among other models.

4. Use Cases and Applications

Object detection with deep learning is very common in our daily life, as we have already seen some examples. Its importance in the modern world is far greater than many initially assume.

Surveillance, Security and Traffic

Data labels aside, object detection in video and live footage is the cornerstone of state-of-the-art surveillance. Computer vision aims to continually exceed expectations, innovating in the detection of theft, traffic violations, suspicious human activity, and more. All these processes are gradually being monitored more effectively than ever before.

car

Object detection is necessary for autonomous driving so that the car can decide whether to accelerate, brake or turn at the next moment. This requires object detection to identify a range of things such as cars, pedestrians, traffic signals, road signs, bicycles, motorcycles, and more.

the medical

Object detection presents a perfect development in the field of medicine, especially radiology. While the technology won't completely replace the need for specialized expertise by radiologists and other specialists, it will dramatically reduce the time it takes to analyze hundreds to thousands of ultrasound scans, and even x-rays, MRIs, and CT scans each day.

retail

Smart inventory management that doesn’t require manual inventory checks, a cashier-less shopping experience, and more retailers implementing object detection computer vision in their stores.

5. Key takeaways

Object detection is where image classification and object localization come together to interpret and label a variety of visuals from images to live footage. Over the past decade, object detection models using deep learning have decreased significantly in processing time and speed, which would not be feasible without CNNs. We can clearly see that object detection is pervasive in applications ranging from the safety features of smartphones to the foundation upon which the next generation of smart cars will depend. After all, object detection models are evolving, growing, and innovating every day to become more accurate and solve more real-time problems in the modern world.

We hope that this basic introduction to object detection with deep learning will serve as a foundation upon which to build further.