Deep Learning Techniques for Object Detection
A report by McKinsey states that by 2030 the global economic activity of Artificial Intelligence would easily sum up to 13 Trillion USD. The field of AI became.
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Key takeaways
- A report by McKinsey states that by 2030 the global economic activity of Artificial Intelligence would easily sum up to 13 Trillion USD.
- It finds and classifies a variable number of the given object or an image.
- Owing to the robust development of deep neural networks, the performance of these detectors have improved rigorously over the years.
Understanding the Term - Deep Learning #
A report by McKinsey states that by 2030 the global economic activity of Artificial Intelligence would easily sum up to 13 Trillion USD. The field of AI became crucial when the machines could complete those tasks that typically required human intelligence. It also includes machines that can learn to acquire skills and gain experience without any human intervention.
Deep learning is another subset of machine learning where artificial neural networks (ANN) and algorithms are inspired by the human brain to learn from massive datasets. Deep learning consists of neural networks which have deep layers that enable learning. Using deep learning techniques, we could solve any problem which requires “critical thinking”.
Why Deep Learning Is Growing Quickly Since Past Five Years? #
At present, the amount of data that we generate every day is striving and estimated to be 2.6 Quintilian bytes. The enormous amount of data generated each day is why deep learning is thriving at a steady rate. As the deep learning algorithms need a lot of data to learn from, it increases the data creation as well as the learning capabilities in recent years.
Also, more data creation means more deep learning algorithm advantages from robust computing power and proliferation of artificial intelligence as a service. Now, let’s move on to one of the most trending deep learning techniques, object detection.
Object Detection with Deep Learning Methods #
Object detection is the procedure to describe a collection of related computer vision modules that includes identifying objects in digital pictures. It tends to locate the presence of objects with bounding boxes, classes, or types of any particular object in an image. When an image is added in the system with one or more objects; it adds a class label for each bounding box. A bounding ‘box? is an imaginary rectangle that creates a collision box for that object and aids as a point of reference for object detection.
The algorithms of object detection produce a list of object categories that are present in the image with an axis-aligned bounding box. It indicates the position and scale of every object category. The two most used methods of object detection are direct object predictions and predictions on a grid. Direct object detection detects and classifies multiple objects. It finds and classifies a variable number of the given object or an image. Few common examples of direct detection are face detection, counting, and visual search engine applications. The predictions on the grid works when the model is properly trained on datasets with full images. Here each grid point independently helps in predicting whether it contains an object’s center or not and if the bounding box dimension is for an object or what object class it is.
The two Most Popular Algorithms of Object Detection are YOLO (You Only Look Once) & SSD (Single Shot Detection)
YOLO is a real-time object detection algorithm that combines a mufti-step process, utilizing a single neural network for performing prediction and classification of bounding boxes for object detection. YOLO is heavily optimized for detection performance and runs faster when two separate neural networks classify and detect objects distinctly. So, basically, YOLO is based on the idea of segmenting an image into multiple small images.
Original diagram for Techfastly by Vin Patel.
SSD is designed for detecting an object in real-time. It utilizes a region proposal network for creating boundary boxes and utilizes those boxes for classifying the objects. So, far below the real-time processing needs, SSD speeds up the process by reducing the need for a region proposal network. For accuracy, SSD applies few improvements which include default boxes and mufti-scale features.
Original diagram for Techfastly by Vin Patel.
The Role of Focal Loss in Object Detection #
Focal Loss is something that tries to handle the class imbalance problem by assigning more weights to hard or easily misclassified examples like a noisy background picture. It reduces the loss contribution from examples and improves the significance of correcting the misclassified examples.
Focal Loss is important because sometimes training is inefficient as most of the locations are easy negatives that have no useful learning. It is proven essential for training the imbalanced datasets in object detection tasks.
Final Thoughts on Deep Learning Techniques for Object Detection #
Object detection is the key ability of most AI and deep learning tactics as mobile robots and general autonomous machines are aggressively being deployed for the purpose of object detection. Hence, object detection systems are gaining more momentum and nan-robots or robots explore the areas which have not been seen by humans yet. In such cases real-time open-world learning ability is critical.
One of the main focus of visual object detection is to detect objects that belongs to specific class targets with absolute localization in an image input. Owing to the robust development of deep neural networks, the performance of these detectors have improved rigorously over the years.
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