YOLO stands for ‘You Only Look Once’ and this algorithm is an excellent object-detection algorithm that uses convolutional neural networks. Before getting into understanding how the YOLO algorithm can be applied for various real-world applications, let us understand what CNNs or Convolutional Neural Networks are.
CNNs are Artificial Neural Networks that use pixel data for processing images or for image recognition. Advanced implementations of deep learning that are based on machine vision use CNNs for descriptive or generative tasks. Similarly, the YOLO algorithm uses CNNs for real-time object detection. It is one of the fastest algorithms for this purpose, capable of working on visual data at up to 155 frames per second. However, at that point, the algorithm becomes less accurate, but at 45 frames per second, it is very accurate yet still remains much faster than alternatives such as Region Proposal Classification Networks (RCNN).
The architecture of the YOLO framework is similar to that of a Fully Convolutional Neural Network, and doesn’t need to process an image multiple times for detecting different classes. YOLO can identify and localise objects in near real-time. This is done by simply passing the image just once through the neural network to gain the prediction or output. The YOLO architecture splits the input image into different grids. It bounds each box and finds a class probability for each bounding box. This is a standard regression problem that uses class probabilities of the detected input images.
In multiple domains such as Defence, Security and Research, the YOLO algorithm has already been adopted for various purposes, for instance, animal detection for wildlife research or traffic light detection in autonomous cars. Cars such as Teslas use the YOLO algorithm and CNNs in order to come to accurate conclusions and respond to various degrees of visual cues, for example, incoming cars or pedestrians.
How does the YOLO algorithm work?
CNNs are Artificial Neural Networks that are similar to Deep Neural Networks (DNNs) but do not have as many complexities as other Feedforward Neural Networks. This is due to CNNs relying on two layers, the feature extraction and the map layer. The inputs of the nodes in the Neural Network extract the local features from each of the preceding layer’s local respective fields. Once this feature extraction is completed, the positional relationships between local and other features are mapped or plotted.
Now, when the YOLO algorithm is applied, each convolutional network starts simultaneously predicting multiple bounding boxes and the class probabilities of the bounding boxes. This entire process is run as a unified model at once on full images. This optimises the detection performance of a single algorithm run, thus, reducing the latency of processes such as detecting objects in a streaming video.
The YOLO algorithm used IoU or ‘Intersection over Union’, that is derived by dividing the areas of the overlaps between the predicted bounding boxes and the ground truths by the areas of unions between the same. Finally, each grid cell that has been initially divided by the algorithm ends up producing set numbers of bounding boxes that are assigned confidence scores and classifications. Since most of the bounding boxes are identified as irrelevant, the final outputs are the bounding boxes that pass the confidence benchmark.
There are other object detection approaches such as Single-ShotMultiBox Detector (SSD) and fast Region-Based Convolutional Neural Networks, but none of these is as fast as the YOLO algorithm. Even though the other two are great at modelling in object detection and have solved the challenge of data limitations, they cannot provide predictions in a single algorithm run like the YOLO algorithm.
CNNs function through learning with the help of training datasets. These training datasets help the nodes in the neural network in setting benchmarks for future computations. For example, if we were to detect objects A and B then we would need a good training data set with a lot of visual data such as thousands of images of A and B. The objects A and B would become targets that would be assigned integer values such as 0 and 1 and would then become class labels. The YOLO algorithm becomes more accurate when the training data set is diverse and has a huge number of input data.
Real-world Applications of the YOLO Algorithm
Here are some real-world implementations of machine vision with the help of the YOLO algorithm:
- Security and Surveillance: The YOLO algorithm has been adopted by police and surveillance systems in order to detect certain people or objects. The algorithm helps in identifying a suspect in real-time and then triggering an alert or the systematic tracking of their movement.
- Research: The YOLO algorithm can be used in all kinds of research. For example, the algorithm can be used to detect movement in wildlife or for tracking targets. The algorithm can even be used to detect spots or patches from geographic data and identify terrain in real-time.
- Autonomous Driving: With YOLO, the Tesla AI is able to use its sensors (LiDAR and cameras) for detecting objects and people around the vehicle. The algorithm can even help identify parking signals and traffic lights. This allows the cars to make autonomous decisions based on the environment and incoming objects such as vehicles.
Eventually, the standard face detection systems in our mobile devices and computers will be replaced by YOLO algorithms. Other technologies such as drones or robots that use machine vision will start adopting YOLO in order to identify humans, objects and other targets in real-time.
Benefits of using YOLO algorithm
The benefits of using the YOLO algorithm are as follows:
- The algorithm helps in improving detection speed.
- YOLO is a very accurate predictive technique that produces minimal background errors and is barely bothered by background noise.
- The algorithm has an amazing capacity to learn the representations of an object.
Conclusion
The YOLO algorithm is used after being trained on entire image inputs, thus, it does not isolate and identify specific objects but rather processes the entire image at once. This enables it to not only encode class appearance data but also gather contextual data. This is why the YOLO algorithm does not get affected by noise or background data when trying to detect specific targets in real-time.
