Objects Detection for Self-Driving Cars using YOLO Algorithm
In the world of trending tech and autonomous uprising capacity of machines, the benchmarks set by both of the advancements mentioned above resulted in bleeding-edge technology, namely Self-Driving Cars or Autonomous Vehicles (AV's). An autonomous vehicle is one that navigates between destinations without the assistance of a human operator, using data from sensors, cameras, radar, and artificial intelligence (AI). Self-driving cars' functionality lies in how the developers use vast amounts of data from machine learning and neural networks. They create and store a map of their surroundings based on various sensors placed on different vehicle parts. The sensors used are Radar, Lidar (light detection and ranging), ultrasonic sensors. After processing all of this sensory data, sophisticated software creates a route and sends instructions to the car's actuators, which control acceleration, braking, and steering.
The main difficulty in AVs comes through the detection of objects which were surrounded by the vehicle.As a result, many algorithms were developed, like HOG (Histograms of Oriented Gradients, 2005), R-CNN (Region Convolutional Neural Network), and YOLO (YOU ONLY LOOK ONCE). Out of them, YOLO is a transparent convolutional neural network for detecting the object in real-time. This method has several advantages compared to the algorithms mentioned above because it looks at the image entirely by predicting the bounding boxes to see the kind of object. As of now, there are four versions of YOLO developed from YOLO v1 to YOLO v4. YOLO is fast and accurate, and thus it outperforms the other algorithms and is so used widely across many aspects like self-driving cars.
How to cite this article:
Kumar NL, Maram B. Objects Detection for Self Driving Cars using YOLO Algorithm. J Adv Res Auto Tech Transp Sys 2021; 5(1): 17-23.
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