Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

Abstract

To enable the vehicle tracking and collision warning system to face more complex road information, the Drosophila visual neural network collision warning algorithm has been improved, including image stabilization algorithm, target region synthesis algorithm, and target tracking algorithm. The results showed that the improved image stabilization algorithm had significantly higher image stabilization quality. The peak signal-to-noise ratio of the stabilized image before improvement was the highest at 80dB and the lowest at 54dB. After improvement, the peak signal-to-noise ratio of the stabilized image was the highest at 82dB and the lowest at 60dB. The improved algorithm did not have any false alarms or missed alarms in collision warning. In video 1, there were false alarms in the unimproved algorithm, while in video 2, there were missed alarms. In video 1, all frames were in a safe state, but the original algorithm displayed an alarm in frames 7-12, 13-22, and 23-31. In video 2, there were dangerous situations in frames 8-24 that required an alarm, while the original algorithm displayed an alarm message in frames 8-17, consistent with the actual situation. The improved target tracking algorithm can complete the task of extracting target motion curves. The target tracking algorithm extracted the motion curves of one target in video 1 and two targets in video 2, which were consistent with the video content. The improvement of the Drosophila visual neural network collision warning model through research is effective, which can improve the driving safety of vehicles in complex road conditions.