Over the past decade, deep learning has gained significant attention and become the dominant technology in the field of artificial intelligence. Object detection online is a significant subfield of deep learning and computer vision with various applications, including object tracking, retrieval, video surveillance, picture captioning, image segmentation, and medical imaging. This tutorial aims to provide a comprehensive introduction to the basics of object detection online for anyone interested in computer vision and object detection. The tutorial is suitable for experienced machine learning engineers seeking implementation guidance, developers looking to expand their knowledge, and product managers interested in exploring the possibilities of object detection.
What is Object detection?
Object detection is a computer vision method that identifies and detects objects of interest within an image or video. Object detection aims to identify objects’ presence, location, and class within an image or video frame. Object detection has various practical applications, such as in autonomous vehicles, surveillance systems, image search engines, and robotics. Object detection algorithms typically use machine learning techniques, such as deep learning, to automatically learn the features of objects and distinguish them from the background of the image or video.
Types of object detection
Object detection can be divided into two categories: machine learning-based approaches and deep learning-based approaches.
Computer vision algorithms are used in more conventional ML-based methods to analyze different aspects of a picture, such as the color histogram or edges, to find clusters of pixels that might be an item. The location of the object and its label are then predicted using these features as input into a regression model.
Contrarily, deep learning-based techniques use convolutional neural networks (CNNs) to carry out end-to-end, unsupervised object detection, eliminating the need for feature definition and extraction using deep learning algorithms such as Convolution Neural Network, Auto-Encoder, Variance Auto-Encoder, etc.
Why is object detection online important?
Object detection, which is a computer vision technique, is closely related to image recognition and image segmentation. These techniques are useful for analyzing and understanding situations in images or videos. However, there are important differences between them. Image segmentation provides a detailed understanding of the elements in a scene at a pixel-level, while image recognition identifies an object and assigns it a class label. Object detection, on the other hand, can locate specific objects within an image or video and count and track them. This makes object detection useful in various scenarios such as crowd counting, self-driving cars, video surveillance, face detection, and anomaly detection. These unique abilities and distinctions of object detection make it a valuable tool in computer vision.
How does object detection online work?
Modern object detection online frameworks often have two stages, many of which have already been pre-trained using the COCO dataset. The COCO picture dataset has 90 unique item categories (cars, persons, sports balls, bicycles, dogs, cats, horses, etc.).
The dataset was created to address prevalent object detection issues. While most of its photographs were taken in the early 2000s, they are now dated because they are smaller, grainier, and contain different things than modern images. The de facto pre-training dataset is replaced with more recent datasets like OpenImages.
Nevertheless, object detection is divided into 2 stages. You must choose between using a single-stage or two-stage network for your object detector, whether you build one from scratch or use one already trained.
In single-stage networks, like YOLO v2, CNN uses anchor boxes to construct network estimates for areas over the entire image. The estimates are then decoded to provide the final bounding boxes for the objects. While substantially faster than two-stage networks, single-stage networks may not reach the same accuracy, particularly in scenarios with few elements.
The first step of two-stage networks, such as R-CNN and its variants, identifies region proposals or image subsets that may include an object. The objects contained in the region proposals are categorized in the second step. Although two-stage networks are often slower than single-stage networks, they can produce precise object detection results.
Applications of Object detection
An introduction of practical use cases for object detection will be given in this section. Most of them have been covered in earlier sections; however, in this section, we’ll go into more detail and examine the potential effects this computer vision approach may have on various businesses.
We’ll specifically look at the following applications of object detection online:
Modern object recognition techniques easily lend themselves to automated video surveillance systems because they can precisely identify and track many instances of a given object in a scene.
Object detection models, for instance, can track many people simultaneously and in real-time as they move around a scene or across video frames. From retail storefronts to industrial manufacturing floors, this kind of granular surveillance may provide vital information regarding security, worker performance and safety, retail foot traffic, and more.
Another useful application of object detection online is crowd counts. Object detection can assist businesses and municipalities in more successfully measuring various types of traffic—whether on foot, in automobiles, or other ways—for heavily crowded locations like theme parks, shopping malls, and city squares.
Companies could optimize anything from store hours and shift scheduling to logistical pipelines and inventory management by employing the ability to find and track people as they move through different places. Object detection could similarly help cities plan events, assign resources, etc.
The best way to explain the object detection online use case for anomaly detection is with examples from a particular industry.
For example, a customized object detection model in agriculture could precisely identify and locate potential plant disease instances, enabling farmers to identify threats to their crop yields that would otherwise not be visible to the unaided eye.
Object detection could also be used in healthcare to assist in treating ailments with distinct and particular symptomatic lesions. Skincare and the treatment of acne are two examples of this; an object detection model could find and identify cases of acne in seconds.
These future use cases leverage and deliver knowledge and information often only available to agricultural experts or doctors, respectively, which is highly significant and persuasive.
The effectiveness of autonomous vehicle systems depends on real-time car detection models. These technologies must be capable of recognizing, locating, and tracking surrounding objects to navigate the world securely and efficiently.
And while tasks like picture segmentation can be applied to autonomous vehicles (and frequently are), object identification still serves as the primary task that supports current efforts to make self-driving cars a reality.
The features of saiwa object detection service
Object detection is a supervised machine learning and machine vision challenge that detects examples of pre-trained classes of objects in videos and images. Saiwa, an artificial intelligence and machine learning-based service platform, provides an online object detection service. The following are the main essential features of Saiwa’s object detection service:
- Delivering cutting-edge algorithms
- Identifying, locating, and segmenting areas of interest (ROI)
- The user has complete control over the level of uncertainty in identified items (by setting the “Confidence” parameter in “Advanced Settings”).
- Exporting and archiving findings on the user’s cloud or locally
- Adjusting the objects
- Retrain the same networks on user-defined datasets using the “Deep Learning” service.
- The Saiwa team requests that the networks be retrained using user-defined datasets utilizing the “Request for Customization” option.
- Apply to multiple images.
Preview and download the detected objects’ images or comprehensive information.
An object detection model can be trained using the cognitive service Custom Vision. You can combine several resource types (for instance, utilizing a Custom Vision (Training) resource to train a model that you subsequently deploy using a Cognitive Services resource) and use a single Cognitive Services resource that may be used for both prediction and training.
To meet the real-time requirements of video processing, object detection online algorithms must not only reliably categorize and localize significant objects, but also be extraordinarily quick at prediction time. The speed of these algorithms has increased significantly over time, from the 0.02 frames per second (fps) of R-CNN to the impressive 155 fps of Fast YOLO.
The original R-CNN methodology is intended to be sped up by Fast R-CNN and Faster R-CNN. R-CNN generates 2,000 potential regions of interest (RoIs) via selective search and runs each RoI through a CNN base separately, which results in a significant bottleneck because CNN processing is relatively sluggish. Fast R-CNN reduces processing time by 20 by first passing the entire picture through the CNN base just once, then matching the RoIs produced by selective search to the CNN feature map. Even though Fast R-CNN is substantially faster than R-CNN, there is still a speed barrier.
Accelerated R-CNN performs object recognition on a single image in about 2.3 seconds, and selective search consumes the entire 2.3 seconds of that time! A separate sub-neural network is used in favour of selective search in faster R-CNN to produce ROIs, adding a 10x speedup and allowing testing to occur at a pace of roughly 7–18 fps.
saiwa object detection service employs two recent deep neural networks: Detecron2 and YOLOv5.
Detectron2 was built by Facebook AI Research (FAIR) as an open-source fast and flexible object detection algorithm . Detectron2 is a ground-up rewrite and successor of the previous Detectron version and it comes from the Mask R-CNN benchmark.
YOLOv5 is the most recent member of “you only live once” (YOLO) family . In object detection task, the YOLO series play an important role in one-stage detectors (i.e. the algorithm requires only one single forward propagation through a neural network to detect objects). At a high level, the detection concept is to split the image into cells, each of which is responsible for predicting multiple bounding boxes along with their corresponding confidence scores.