A Lidar-Vision Fusion Target Detection Model for Low-Light Environments

In the field of autonomous driving and specific environments, target detection is a critical task module. Currently, the mainstream approach to target detection is to use deep learning to train specific network models, enabling them to recognize targets. Although many effective network models have been proposed by scholars, there are few target detection networks tailored for low-light environments. In practical applications, complex lighting changes can lead to decreased accuracy in target detection. This paper combines a low-light enhancement network with a LiDAR-camera fusion target detection network to achieve target detection in low-light environments and validates the algorithm using the Nuscenes benchmark dataset. The experimental results demonstrate that the improved network exhibits greater robustness in target detection under complex lighting conditions.