Auxiliary lighting system for night vehicle driving based on computer vision
A computer vision and vehicle driving technology, which is applied to vehicle components, optical signals, signal devices, etc., can solve problems such as the inability to provide safe lighting distance, the lighting distance of car lights is not up to standard, and traffic accidents are prone to occur, so as to improve visual visibility, The effect of improving clarity, improving work efficiency and safety
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Embodiment 1
[0023] Such as figure 1 As shown, a computer vision-based auxiliary lighting system for nighttime vehicle driving includes an information collection module, a Raspberry Pi control module and an auxiliary light dynamic control module. The three are connected in sequence. The information collection module here uses an external camera, because The resolution of some driving recorders is low, so it is better to choose an external camera, which can not only improve the driving experience, but also improve the visual recognition of the vehicle. The Raspberry Pi control module is equipped with a computer vision module and a vehicle information monitoring module. It is integrated with the image recognition module and the communication between the three. When the external camera captures an obstruction on the opposite or front road, the collected road information is sent to the Raspberry Pi control module. At this time, the computer vision module in the module uses OpenCV to collect Th...
Embodiment 2
[0026] The difference between this embodiment and Embodiment 1 is that in the above-mentioned computer vision module, the pattern recognition technology is mainly used, that is, the image is divided into predetermined categories according to the statistical characteristics or structural information extracted from the image. In the module, it is necessary to train a classifier and collect a large number of samples of cars driving at night, including positive samples and negative samples. After many repeated tests and inspections, we believe that the ratio of positive samples to negative samples is 1:2. Train the classifier with higher accuracy and wider range of use. By training the classifier, find out the commonality of the samples, generate a file called .xml, and then reference it in the Python language, so as to better realize the processing of vehicle information .
[0027]At the same time, the vehicle information monitoring module in the above embodiment mainly monitors ...
Embodiment 3
[0030] Such as figure 2 As shown, an auxiliary illuminator for nighttime vehicle driving based on computer vision, including a camera, Raspberry Pi and auxiliary lights, the three are connected in sequence, the camera is used to collect road information, and the collected information is transmitted to the Raspberry Pi, the tree After the internal processing of the Raspberry Pi system, the dynamic control of the GPIO is realized, and the output signal is connected to the auxiliary light to complete the intelligent control of the auxiliary light; among them, the Raspberry Pi is equipped with a computer vision module, a vehicle information monitoring module and an image recognition module. The communication of the three is integrated; the computer vision module uses OpenCV to process the collected road information, and the processed information is sent to the vehicle information monitoring module for detection and identification. Through the image recognition module, Python langu...
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