Being a part of the Computer Vision technology, Object Detection helps to identify and locate objects in images and videos. Humans require only a few seconds to locate and identify objects of interest whereas machines need the training to replicate the human vision. Here is where object detection algorithms play an important role in helping machines to detect objects in images.
Object detection is often confused with image recognition but they are two different entities with a clear distinction in between. Image recognition involves identifying the objects in an image using pre-defined labels whereas object detection in addition to identifying the objects, also draws a shape around them and labels the object inside the box thereby giving additional info than image recognition.
How Does Object Detection Work?
Let’s explore a few simple algorithms to understand how object detection works;
R-CNN helps to locate objects in images and comprises of the following three modules:
By using the Selective Search approach, the R-CNN algorithm extracts regions within an image. Selective Search identifies 4 regions of an object like scales, textures, enclosures, and colors. Based on these patterns, various regions are proposed. Selective Search works as below:
- In this approach, sub-segmentation is generated that help to detect multiple regions within an image
- Later, based on scales, textures, colors, and enclosures, similar regions are merged to form a larger region.
- With the help of the larger regions, the location of the object is identified.
- About 2000 regions are extracted from an image in this approach.
A CNN-based classifier is used to reshape the proposed regions based on the input fed into to the CNN. This helps to extract feature vector with fixed length from every region.
Each region in the image is finally classified using linear support vector machines.
Fast R-CNN also uses the Selective Search approach for generating region proposals. But its architecture unlike R-CNN has high mean average precision and supports single-stage training. This algorithm doesn’t require disk storage for feature caching and updates all network layers with training.
- Fast R-CNN processes an image with convolutional layers and max-pooling by taking object and image proposals as input and generates convolutional feature maps.
- Fixed-length feature vectors are extracted from every feature maps that are then fed to the fully connected layers.
- 2 output layers are used over a fully connected network; a linear regression layer to output bounding box coordinates for classes and a softmax layer to output classes.
A modified version of Fast R-CNN, Faster R-CNN uses region proposal network (RPN) instead of Selective Search for generating regions of interest.
- An image is fed into the convolutional layer as input and this helps to generate feature maps for that image.
- The feature maps are then passed through RPN for generating object proposals
- The object proposals are passed through the ROI pooling layer to bring down all proposals to the same size.
- Proposals are then run through a fully connected network enabling the softmax later to output the classes and linear regression to output bounding boxes.
Following are some of the common use cases of Object detection:
Anomaly Detection for Healthcare and Agriculture
Object detection helps to treat specific skin conditions. For instance, it can be used to treat acne where it helps to locate and identify the instances of acne in just a few seconds.
Custom object detection models can be created for the detection and identification of potential instances of plant or crop diseases. This helps farmers to detect threats to the yields that otherwise cannot be detected by the naked human eye.
Certain object detection techniques can identify and track multiple instances of objects accurately in a scene and can be used in automated video surveillance systems. These models can locate and track multiple people in real-time and at once across video frames. This type of tracking at the granular level helps to provide actionable insights on the performance and safety of workers, security and foot traffic for industrial factory floors and retail stores, etc.
To move efficiently and safely on roads, self-driving cars must have the ability to locate, identify, and track objects in their surroundings. For this, they would require the help of object detection models. The performance and success of the self-driving cars depend on the accuracy of the object detection models to detect in real-time.
Data labeling techniques such as image segmentation can be used to train autonomous vehicular models, object detection models act as the foundation for making them a reality.
Crowd counting is an amazing use case of object detection and helps to localize and track people as they navigate through various spaces. Businesses can measure the different types of traffic in highly populated areas like theme parks, city squares, and malls. It can help them to optimize their inventory management, shift scheduling, logistics pipelines, and their store timings.
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