Audio detection method for road tunnel traffic events

Abstract

The invention discloses an audio detection method for road tunnel traffic events. Audio data collected in a road tunnel is divided into frames, second-level long-term and short-term memory network models are utilized to improve the identification rate and robustness, specifically, 1), the first long-term and short-term memory network model A is utilized to optimize extracted audio features, and the attention mechanism is further introduced to improve robustness of the features; and 2), the second long-term and short-term memory network model B is utilized to weight the optimized and refined features, the output result is further optimized, so road tunnel traffic event detection has high timeliness and accuracy, and the method has good application prospects.

Description

Technical field [0001] The present invention relates to the technical field of traffic incident detection, in particular to an audio detection method for road tunnel traffic incidents. Background technique [0002] The structure of road tunnels is different from general road constructions. It has a series of undesirable characteristics such as relatively narrow internal roadbed width, strong space tightness, small field of view, and low visibility, which results in a more complex driving environment and the resulting traffic accident pattern of road tunnels. , Mainly include rear-end collision, car rollover, wall collision, fire and explosion, cargo throwing, etc. Among them, rear-end collision is the main form of road tunnel traffic accidents. [0003] In the prior art, accidents in highway tunnels can be discovered in time through technical detection methods. For example, through traditional video surveillance technology, relying on manual viewing of video images, the location of...

AI Technical Summary

Problems solved by technology

[0004] At present, in the prior art, patent No. CN201510324584 discloses a detection method of fast acoustic events under driving noise environment, which uses conventional speech noise reduction and feature The extraction method does not take into account the reverberation in the noise of the road tunnel, and does not optimize the features, etc., and is not suitable for traffic event detection in road tunnels; Patent No. CN201710069291 discloses an audio-based traffic event detection device and method, However, this method pays more attention to the front-end acquisition method, that is, the array is used to collect audio data, and beamforming is used to reduce noise. However, it is difficult to quickly locate and beamform vehicles traveling at high speeds, especially for road tunnel traffic incident detection. Poor accuracy; Patent No. CN201410668501 discloses a fast acoustic event detection system in a traffic noise environment. This method mainly focuses on the elimination of wind noise, which has certain limitations and is also not suitable for traffic event detection in road tunnels

[0005] To sum up, the three patents introduced above all use more traditional sound noise reduction strategies to improve the quality of front-end voice collection, and the back-end recognition algorithm is mainly support vector machine and convolutional neural network, the applicable environment is limited, the main problem is that the traditional sound noise reduction strategy cannot be trained and has certain limitations, while the recognition ability of the traditional pattern recognition algorithm has a great impact with the environment changes, and the robustness is poor; Moreover, the above three methods do not involve any method of optimizing features, and how to improve the recognition efficiency and robustness through feature weighting, so as to improve the accuracy of traffic incident detection based on audio, is currently urgently needed to be solved

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