An accurate low-light object detection method based on pyramid networks

Abstract

Low light object detection is a challenging problem in the field of computer vision and multimedia. Most available object detection methods are not accurate enough in low light conditions. The main idea of low light object detection is to add an image enhancement preprocessing module before the detection network. However, the traditional image enhancement algorithms may cause color loss, and the recent deep learning methods tend to take up too many computing resources. These methods are not suitable for low light object detection. We propose an accurate low light object detection method based on pyramid networks. A low-resolution pyramid enhancing light network is adopted to lessen computing and memory consumption. A super-resolution network based on attention mechanism is designed before Efficientdet to improve the detection accuracy. Experiments on the10K RAW-RGB low light image dataset show the effectiveness of the proposed method.