Deep learning-based low-light image enhancement method for driving behavior recognition^*

When image acquisition is carried out in low-light scenes, there are often problems like not enough light and too much noise, which brings difficulties to the subsequent detection of distracted driving behavior. In this paper, Zero-DCE is adopted as the low-light image enhancement model, and deconvolution layer is added to compensate the details and restore the fine structure in the image, thus improving the brightness and clarity of the image. The residual block is introduced to reduce noise and improve clarity, and the kernel selection module is added to solve the noise problem. The kernel selection module can adaptively select the appropriate kernel function to reduce the impact of noise. Finally, the image quality assessment methods PSNR and SSIM are used for testing and analysis. SSIM increased by 42.62%. The results indicate that the improved method can increase the brightness of the image effectively, reduce noise effects and recover key details, laying a foundation for subsequent distracted driving detection methods.