Deep Learning Techniques for Image Recognition in Autonomous Vehicles
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Abstract
The rapid advancement of autonomous vehicle technology has brought deep learning techniques to the forefront of image recognition systems, enabling vehicles to perceive and navigate their environment. This paper explores the application of deep learning models, particularly convolutional neural networks (CNNs), in image recognition for autonomous vehicles. These models are pivotal in interpreting visual data from cameras and sensors, enabling tasks such as object detection, lane detection, pedestrian recognition, and traffic sign identification. The integration of deep learning algorithms enhances the vehicle's ability to make real-time decisions, improving safety and driving efficiency. We discuss the key challenges faced in deploying deep learning models, such as the need for large labeled datasets, computational power, and model interpretability. Additionally, we highlight recent advancements and the potential for future improvements in accuracy and robustness, particularly in complex, real-world driving environments. This paper aims to provide insights into how deep learning is transforming autonomous vehicle technology and its potential to revolutionize the future of transportation.
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