Object detection is a part of Computer Vision technology that helps in identifying and locating objects in videos or images. Humans can easily locate and recognize objects of interest within few seconds. Similarly, object detection algorithms locate instances of objects in a given image thereby allowing machines to replicate the human vision.
Object detection and image recognition are often used interchangeably but are two different entities with a clear distinction between them. While image recognition is used for labeling images, object detection draws a shape like a box around the object and then labels the box. Moreover, object recognition identifies where each object is and what label is applicable thereby giving more information than image recognition.
How Does Object Detection Work?
We will explore some of the simple algorithms that are used for object detection to understand how it works;
R-CNN proposes bounding boxes in the image and verifies if any of these boxes have any objects. It comprises of three modules which are as follows:
R-CNN algorithm uses Selective Search approach for extracting boxes/regions from an image. The Selective Search identifies 4 basic regions of an object such as colors, textures, scales, and enclosures and proposes various regions based on these patterns. Below is the step-by-step brief of Selective Search works;
- It generates sub-segmentations initially which helps to identify multiple regions from an image.
- Later, it merges similar regions to form a larger region based on colors, textures, scales, and enclosures.
- These larger regions then help to identify the region of interest or object location.
- It extracts about 2000 region proposals from an image.
The proposed regions are then fed into a CNN-based classifier where the regions are reshaped as per the input of CNN. It then extracts feature vector having fixed-length from each region.
Linear support vector machines are finally used to classify each region in an image.
Similar to R-CNN, this approach also uses the Selective Search for generating object proposals. But the architecture of Fast R-CNN supports single-stage training, has higher mean average precision, feature caching doesn’t require disk storage and training helps to update all network layers.
- Fast R-CNN takes the image and object proposals as input and processes the image with max-pooling and convolutional layers to generate convolutional feature maps.
- This is followed by the extraction of fixed-length feature vector from each of the feature maps which are then fed to fully connected layers.
- Two output layers are used on top of a fully connected network; a Softmax layer to output classes and a linear regression layer to output bounding box coordinates for classes.
Faster R-CNN is a modified version of Fast R-CNN that uses Region Proposal Network (RPN) for generating Regions of Interest instead of Selective Search;
- An image is passed on to the convolutional layer as input which helps to generate feature maps of that image.
- Feature maps are run through the RPN which generates object proposals with an objectness score.
- To bring down all proposals to the same size, object proposals are run through the ROI pooling layer.
- Proposals are then passed on to the fully connected network where the Softmax layer outputs the classes and the linear regression layer outputs bounding boxes for objects.
Object detection has been already put to use in the following areas:
Self-driving cars should have the ability to detect, locate and track objects surrounding them to move on the roads efficiently and safely. For this, they rely heavily on object detection models. The success of autonomous vehicular systems depends on the accuracy of car detection models that can detect in real-time.
Even though data labeling techniques like image segmentation also helps to train autonomous vehicles, object detection acts as the foundation for making self-driving cars a reality.
World-class object detection techniques can detect and track multiple instances of an object in a scene accurately and hence form the basis for automated video surveillance systems. These models can detect and track various people all at once and in real-time as they move across video frames. This kind of granular tracking helps to provide actionable insights for the performance and safety of workers, security, foot traffic at retail outlets, etc for retail stores, factory floors in the industrial sector, etc.
Detection of Anomalies in Healthcare, Agriculture
Object detection models can be used for acne treatment where the model helps to locate and detect the instances of acne within few seconds thereby helping to treat specific skin conditions.
Custom object detection models can be used to detect and identify potential instances of crop or plant diseases that help farmers to identify threats to their yields which are otherwise non-detectable by the naked human eye.
Crowd counting is a valuable use case of object detection that helps to localize and track people as they move through various spaces. It helps businesses to measure various types of traffic in densely populated areas like malls, city squares, and theme parks.
Object detection can help businesses optimize their store timings, inventory management, logistics pipelines, and shift scheduling.
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