The latest advancement within the artificial intelligence (AI) industry has popularised deep learning and computer vision to an extensive level. It is because of these advanced technologies that the application of object detection has become easy to create, which was previously conceived to be challenging.
What is computer vision?
Computer vision is a branch of artificial intelligence that enables systems and computers to serve helpful information from video, digital images and several visual inputs. It helps in taking actions and making recommendations depending on the perceived information.
Object detection within computer technology is associated with image processing and computer vision, which deals with identifying instances related to semantic objects of certain objects like buildings, cars or humans in digital videos and images.
With the advancement of AI, technologies like computer vision have evolved to be this important and assist in object detection. Computer vision has flourished with new algorithms and hardware, magnifying object detection’s accuracy. According to Towards Data Science, within a decade or less, present systems reach an accuracy level of 99% from 50%. This has helped object detection to be more specific and accurate than human beings at swiftly reacting to different visual inputs.
Read more: “What is computer vision? Some real-world examples, applications & advantages”.
What is object detection?
The computer vision technique that aims at identifying and locating objects on a video or a picture is called object detection. Computers can process data much quicker than humans; still, it is challenging for computers to identify and detect different objects on a video or image. It is difficult for computers because they only interpret the bulk of outputs in binary language.
This blog will help you understand these advanced technologies like object detection and computer vision and how they have been used in real-world applications.
Difference between image classification and object detection
The image classification technique is different from that of object detection. In the case of image classification, only one object can be detected; for instance, by seeing an image of a dog, it can be classified as a picture of an animal and a dog. As far as a single object is present in an image, image classification is used.
On the other hand, when in an image or a video, multiple objects are present, then the technique of object detection is used. It functions by putting rectangular boxes surrounding the specified object, and this technique can help the machine identify the objects within each box. With the usage of object detection, the objects’ exact locations can be indicated. Such a concept can be used for solo pictures comprising different objects; therefore, several boxes can be shown.
Object detection applications are unlimited and can detect and identify real objects like buildings, cars, human beings and so on. In addition, machines require several labelled data related to various types of objects to identify all the objects in the future. Machine learning models trained on labelled datasets have an improved chance of making accurate predictions.
In recent times, several organisations have come up that provide data annotation services; therefore, based on the requirements, accurate data annotation should be selected. Such a technique is broadly applied to object/people tracking applications, surveillance video cameras and so on.
Models for object detection
There are several object detection models; let’s have a proper insight into the popular ones.
R-CNN, Mask R-CNN, Faster R-CNN
One of the most famous object detection models is the household of regional-based CNN models. Such a model revolutionalised how the object detection world used to function. In the last years, they have become spot on in accuracy and more efficient.
YOLO and SSD
A series of models which belong to the detector family of a single shot were issued in 2016. The SSDs are quicker than the CNN models; however, their accuracy level is significantly lower than that of CNNs.
‘ You only look once ’, popularly known by its acronym ‘YOLO’, differs from region-based algorithms. Similar to SSDs, YOLO is quicker as compared to R-CNNs; however, it falls behind due to low accuracy. In cases of embedded devices or mobiles, SSDs are best-suited.
Recently, object detection (OD) models are becoming increasingly popular. CentreNet abides by an approach of key point-based for detecting objects. Such, when compared with R-CNN or SSD approaches, it proves more efficient and accurate as well. There is only one drawback to such a method: its slow training process.
Advantages of real-world applications of object detection
OD is interconnected with similar CV techniques like image recognition and image segmentation, which permit the analysis and understanding of the various scenes in images and videos. In contemporary times, different use cases of the real world are applied in the OD market, which has tremendously impacted multiple industries.
Let’s dive and get a proper insight into real-world OD applications and how such affect certain areas.
OD in real-time and monitoring the objects’ movements permit surveillance video cameras to track and record scenes of specific locations like an airport. Such an Avant-garde technique helps precisely locate and recognise different instances related to a certain object within a video. Just like the objects move in real-time through a particular situation or cover a given frame, the OD system helps store information with real-time monitoring feeds.
One of the major causes behind the triumph of autonomous cars is the real-time OD models, which are based on AI. Such systems help in locating, identifying and tracking objects surrounding them. Such assists in different purposes of efficiency and safety.
Several applications for anomaly detection are available in recent times that use OD. Several industries make use of these; for example, in the agricultural sector, OD models are used to precisely identify and search for potential plant disease instances. With OD application, farmers get notified and can save their crops from deadly threats.
Another eminent instance of anomaly detection application is recognising symptomatic lesions and skin infections. Few applications are being built already and used in acne treatment and skin care, which uses OD applications.
Notable read: “10 most prominent computer vision applications to watch in 2023”.
In cases of severely populated and congested areas like airports, theme parks, city squares or shopping malls, OD application performs beyond imagination. Normally, such OD application proves useful for municipalities or large enterprises for monitoring road traffic, the number of cars passing within a specific time frame, and tracking violations of laws.
Creating any object detection model is complex and can encounter several challenges. However, there are solutions which can mitigate the challenges accurately.
Challenges which are generally faced while OD modelling is dual synchronisation, numerous aspects ratios & spatial scales, real-time speed detection, and limited data. However, all the issues can be limited effectively by using the required solutions.
OD is perceived to be more difficult than classification. Several studies have been conducted to overcome these issues to yield magnificent results; however, such issues continue to exist. Surely, OD models face issues detecting small objects, specifically the ones gathered together with fragmented or partial occlusions.
Detection in real-time alongside localisation accuracy and object classification is a significant problem. However, on an optimistic note, it can be concluded that video tracking may witness further extensive advancement in the upcoming future in several contexts.
With proper advancement in technology, the issues limiting the real-time application of object identification will be dealt with appropriately, helping it to flourish further and applied in several other usages.