With recent advancements in the artificial intelligence industry, computer vision and deep learning have gained a lot of attention. To their credit, object detection apps, which were previously considered extremely difficult, have become easier to build.
Object detection can be defined as a computer vision technique that aims to identify and locate objects on an image or video. Computers may be able to process information much faster than humans, however, it is still difficult for computers to detect various objects in an image or video. The reason is that the computer interprets the majority of the output in binary language only.
This article aims to briefly discuss:
- The basics of object detection
- Object detection models
- The benefits of object detection
- The challenges and the solutions
Before we get to the above points, we need to understand the difference between image classification and object detection. Newbies tend to confuse these two.
Difference between object detection and image classification
Let’s break down these techniques, to know the difference between them. When you look at a picture of a dog, you can instantly tell it is a picture of an animal i.e. what the picture is about. This is what image classification is for.
As long as there is only one object, image classification techniques can be used.
But if we have multiple objects, this is when the concept of object detection comes into play. By building rectangular boxes around the object of interest, we can help the machine recognize the object that each box contains. We can also indicate the exact location of the objects using this method. It is possible for a single image to contain many objects, so multiple bounding boxes can be displayed.
Object detection applications are limitless, but they usually identify and detect real objects such as humans, buildings, cars and many more. In addition, a machine needs a lot of labeled data different types of objects so that he recognizes these objects in the future. This means that the ML model trained on this labeled data set will have a better chance of making accurate predictions.
In addition, several companies offer data annotation services. You just have to choose the right one according to your needs. This technique is widely applied in people / object tracking applications, CCTV cameras that I will develop later.
Object detection models
Now that we’ve got the definition of object detection, let’s take a look at some popular object detection models.
R-CNN, Faster R-CNN, R-CNN Mask
The most popular object detection models belong to the regional CNN model family. This model revolutionized the way the world of object detection worked. In recent years, they have become not only more precise, but also more efficient.
SSD and YOLO
There are a plethora of models belonging to the one-shot detector family that were released in 2016. Although SSDs are faster than CNN models, their accuracy rate is much lower than that of CNN.
YOLO, or you only watch once, is quite different from region-based algorithms. Like SDDs, yolo is faster than R-CNNs but lags behind due to low accuracy. For mobile or in-vehicle devices, SDDs are the perfect choice.
In recent years, these object detection models have gained in popularity. CentreNet follows a key point-based approach to object detection.
Compared to SSD or R-CNN approaches, this model is more efficient and accurate. The only downside to this method is the slow training process.
Benefits of real-world object detection
Object detection is completely related to other similar computer vision techniques such as image segmentation and image recognition that help us understand and analyze scenes from videos and images. Nowadays, several real use cases are being implemented in the object detection market, which has a huge impact on different industries.
Here, we will specifically examine the impact of object detection applications in the following areas.
The main reason for the success of autonomous vehicles lies in models based on artificial intelligence of real-time object detection. These systems allow us to locate, identify and track the objects around them, for the sake of safety and efficiency.
Real-time object detection and object movement tracking allows CCTV cameras to track recording scenes from a particular location such as an airport. This advanced technique accurately recognizes and locates multiple instances of a given object in the video. In real time, as the object moves in a given scene or in a particular image, the system stores the information with real-time tracking streams.
For heavily populated areas such as shopping malls, airports, city squares, and theme parks, this app works amazingly well. Generally, this object detection app is useful for large businesses and municipalities to track road traffic, law violations and the number of vehicles passing in a particular time frame.
Detection of an anomaly
There are several anomaly detection apps available for different industries that use object detection. For example, in agriculture, object detection models can accurately recognize and find potential cases of plant disease. With the help of this, the farmers will be informed and they will be able to protect their crops from such threats.
As another example, this model was used to identify skin infections and symptomatic lesions. Some apps are already designed for skin care and acne treatment using object detection models.
Keep in mind that there are some issues that can arise when creating any type of object detection model. However, solutions are also available to limit the challenges.
Object detection modeling challenges and solutions
The first challenge for object detection is to classify the image and position of the object, which is known as object location. In order to solve this problem, most developers often use a multitasking loss feature to penalize both localization errors and classification errors.
- Solution: Regional convolutional neural networks display an object detection framework class that consists of proposals for generating regions where objects are likely to be located, followed by processing CNN models to classify and rectify object locations. The Fast-R CNN model may improve initial results with R-CNN. As the name suggests, this Fast R-CNN model offers tremendous speed, but the accuracy also improves only because the tasks of locating and classifying objects are optimized using a loss function. multitasking.
Real-time detection speed
The fast speed of object detection algorithms has always been a major issue to classify and locate crucial objects accurately at the same time to respond to real-time video processing. Over the years, several algorithms have improved the test time from 0.02 frames per second to 155 fps.
- Solution: The Faster R-CNN and Fast R-CNN models aim to accelerate the initial speed of the R-CNN approach. Since R-CNN uses selective search to produce 2000 candidate regions of interest and goes through each CNN-based model individually, this can cause a significant bottleneck as model processing fails. While the Fast R-CNN model transmits the entire image through the CNN database once, then matches the ROIs created with selective search on the feature map, considering a 20-fold reduction in processing time.
Multiple aspect ratios and spatial scales
For many object detection applications, the features of interest can appear in a wide range of proportions and sizes. Researchers have proven numerous methods to ensure detection algorithms capable of recognizing different objects at different views and at different scales.
- Solution: Rather than a selective search, faster R-CNN has been updated with a region proposition network that uses a small sliding window on the map of the image’s convolutional features to produce candidate regions of interest. Multiple regions of interest can be predicted at different positions and described relative to reference anchor boxes. The size and shape of these anchor boxes are selected to cover a range of aspect ratios and different scales. It allows several types of objects to identify with the expectation that the coordinates of the bounding box do not need to be adjusted during the locate task.
One of the undeniable facts to consider is the limited amount of annotated data which becomes an obstacle in building an application. These datasets specifically contain examples of ground truth for tens to hundreds of objects, while image classification datasets include approximately 100,000 different classes.
- Solution: Well, there are several image datasets available on the internet, like COCO Dataset, offered by Microsoft which is currently leading some of the object detection. annotated data. This dataset contains 300,000 images segmented with 80 different object categories according to precise location labels. Each image contains an average of 7 objects and items that appear on a very large scale. One of the most interesting ways to reduce data sparseness is YOLO9000, the second version of YOLO. YOLO9000 addresses many crucial updates in YOLO, but it also aims to reduce the dataset gap between image classification and object detection. In addition, it trains ImageNet and COCO in parallel, an image classification dataset with tens of thousands of object classes.
According to sources, object detection is considered much more difficult than classification, especially because of the problems mentioned above. Researchers continue to work hard to alleviate these barriers, which at times have yielded startling results; however, significant problems persist. Granted, all object detection models struggle with small objects, especially those collected with partial occlusions. Real-time detection with object classification and location accuracy is always a notable issue, and researchers often prioritize one or the other when making design decisions. On an optimistic note, video tracking could see further progress in the future in a variety of other settings.
In this article, I have tried to briefly cover the basics of object detection techniques. I really hope you found this short article useful.