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In recent years, with the development of social economy and the unprecedented development of computer hardware computing power, artificial intelligence technology has developed rapidly, the era of big computing has come, and breakthroughs in deep learning algorithms have brought new opportunities for human-computer interaction and autonomous vehicles. Autonomous driving is divided into three parts: perception, planning and control. The perception part is the most widely used technical field of computer vision, and the camera, as one of the indispensable sensors in intelligent driving vehicles, provides important image information for self-driving vehicle systems. The automatic driving system can obtain the location and distance information of various vehicles and pedestrians appearing in front of the car, providing accurate road information for drivers, avoiding traffic accidents, reducing casualties and property losses. This paper studies the object detection task and depth estimation task based on monocular vision in the visual distance perception system of autonomous driving. The emergence of deep learning has promoted the rapid development of the field of computer vision, and the application of deep convolutional neural networks has enabled vision-based object detection and depth estimation. The estimation accuracy has been greatly improved, but the computational load of the deep convolutional neural network is very large. Since the automatic driving system needs to control the car in real time, it requires the visual perception algorithm to run in real time, and the on-board computing platform of the self-driving car is required. It belongs to the mobile computing platform, and its computing power is very limited. Therefore, this paper designs a multi-task deep convolutional neural network to detect the targets appearing in the field of view of the autonomous vehicle in real time based on monocular images, and obtain the front The distance between the target vehicle or person and the camera of the vehicle appears. |
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Target detection
Detection and tracking algorithms
Data modeling
Image analysis
Visual process modeling
Performance modeling
Convolutional neural networks
