Object Detection presents a computer vision approach for detecting things in photos and movies. To obtain relevant results, object detection algorithms often use machine learning or deep learning. We can detect and find items of interest in photos or video in a handful of seconds when we glance at them. The purpose of object classification is to use a computer to imitate this intelligence.
Importance of Object Detection
Object detection applications are crucial for ADAS- advanced driver assistance systems, which allows automobiles to recognize driving lanes and pedestrians to improve road safety. Different types of object detection algorithms are also beneficial in video surveillance and picture retrieval applications.
To get began with object recognition using deep learning, you can choose one of two approaches:
- Make a bespoke object detector and train it. To build a bespoke object detector from the ground up, you must first create a network architecture that can learn the features of the objects of interest. To train the CNN, you’ll need a big amount of labeled data. A bespoke object detector can produce amazing results. However, you must manually configure the weights and layers in the CNN, which takes a long time and a lot of training data.
- Use an object detector that has been pre-trained. Many deep learning object detection procedures use transfer learning, which allows making with a pre-trained model structure and afterward fine-tuning it for your project. So because object detectors have previously been trained on dozens, if not millions, of photos, this technique can produce speedier results.
Whether you construct a new object detector or utilize one that has already been trained, you must choose between a two-stage network and a single-stage network for your object detection neural network.
The CNN provides network predictions for areas throughout the whole picture using anchor boxes in single-stage networks like YOLO v2, and the predictions are decoded to construct the finalized bounding boxes for the items. Single-stage networks are often quicker than two-stage one, but they don’t always achieve the same degree of accuracy, especially in situations with little objects.
Two-stage types (R-CNN for example) and their derivatives, identify region suggestions, or subsets of the picture that may contain an object, in the first stage. The structures within the region suggestions are classified in the second step. Two-stage networks are capable of producing extremely precise AI object detection results, although they are often faster than single-stage networks.
Object detection and ML
Machine learning algorithms are also widely employed for object recognition, and they vary from deep learning in that they use a different approach. The following are examples of common object recognition machine learning techniques:
- ACF features
- SVM classification utilizing HOG features
- The Viola-Jones method for detecting a human body
You can begin with a pre-trained model object detector or construct a bespoke object detector to fit your application, equivalent to DL–based techniques. When employing machine learning, you’ll have to manually pick the identifying characteristics for an item, as opposed to automatically selecting features in a DL–based process.
The optimal method for detecting objects is determined by your problem. When deciding between ML and DL, the most important factor to consider is if you have a strong GPU and a large number of labeled training pictures. If you answered no to both of these questions, machine learning could be a better option. When you have more photos, deep learning approaches perform better, and GPUs reduce the time it takes to test the network.