Traffic signal lamp identification method and device based on artificial intelligence, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence and image detection, and provides a traffic signal lamp identification method and device based on artificial intelligence, equipment and a medium. Target feature information of a target image subjected to size processing can be extracted by utilizing a darknet53 network and is input into a traffic signal lamp recognition modeltrained by adopting a Mixup algorithm and a residual attention network; the target feature map is output to accurately extract detail features, the recall rate and accuracy are improved; the recognition of the target anchor box on each target feature map is obtained; the target anchor box coordinates and the target score are output, the target anchor box coordinate with the highest score servesas the predicted position coordinate and is mapped to the to-be-recognized image, the recognition result is obtained, automatic recognition of the traffic signal lamp is achieved based on an artificial intelligence means, and the recognition accuracy is higher. The invention also relates to a blockchain technology, and the recognition result and the traffic signal lamp recognition model can be stored in the blockchain. The method can also be applied to a smart traffic scene, thereby promoting the construction of a smart city.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence and image detection, in particular to an artificial intelligence-based traffic signal recognition method, device, equipment and medium. Background technique [0002] With the increasing number of illegal activities such as running red lights, the rapid positioning and identification of signal lights in traffic checkpoint images has become an extremely important and challenging task in urban traffic management. [0003] The traditional YOLOv3 detection algorithm has become one of the mainstream target detection algorithms in the industry due to its advantages of fast detection speed and good accuracy. There is still a lot of room for improvement in the effect, especially for detection and recognition in complex scenes such as night or haze, the effect is weak. Contents of the invention [0004] In view of the above, it is necessary to provide an artificial intelligence-ba...

AI Technical Summary

Problems solved by technology

[0003] The traditional YOLOv3 detection algorithm has become one of the mainstream target detection algorithms in the industry due to its advantages of fast detection speed and

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