MEGF-Net: multi-exposure generation and fusion network for vehicle detection under dim light conditions

Abstract

Vehicle detection in dim light has always been a challenging task. In addition to the unavoidable noise, the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the problem more difficult. Conventional image enhancement methods may produce over smoothing or over exposure problems, causing irreversible information loss to the vehicle targets to be subsequently detected. Therefore, we propose a multi-exposure generation and fusion network. In the multi-exposure generation network, we employ a single gated convolutional recurrent network with two-stream progressive exposure input to generate intermediate images with gradually increasing exposure, which are provided to the multi-exposure fusion network after a spatial attention mechanism. Then, a pre-trained vehicle detection model in normal light is used as the basis of the fusion network, and the two models are connected using the convolutional kernel channel dimension expansion technique. This allows the fusion module to provide vehicle detection information, which can be used to guide the generation network to fine-tune the parameters and thus complete end-to-end enhancement and training. By coupling the two parts, we can achieve detail interaction and feature fusion under different lighting conditions. Our experimental results demonstrate that our proposed method is better than the state-of-the-art detection methods after image luminance enhancement on the ODDS dataset.