Night-Time Vehicle Detection Based on Hierarchical Contextual Information

Night-time vehicle detection, which forms a basic component of the intelligent transportation system, is a topic of intense research interest with multifarious challenges. Due to the presence of low-light conditions, vehicles are typically indistinguishable from the background, and interference from light sources often arises in this complex environment. Existing widely used deep learning-based object detection models are designed for daytime scenarios and they have seldom considered these problems. Based on an investigation of current detection techniques and an analysis of the specific challenges of night-time vehicle detection, we propose a hierarchical contextual information (HCI) framework that can be used as a plug-and-play component to improve existing deep learning-based detection models under night-time conditions. Our HCI consists of three parts, an estimation branch, a segmentation branch and a detection branch, and can be applied to excavate hierarchical contextual clues and fuse them for the detection of vehicles in night-time environments. In each module, the context and predictions are extracted at the image-level, the pixel-level, and the object-level, respectively, and the results from each are complementary and beneficial to each other. Comprehensive experiments on two scenes from the Berkeley Deep Drive (BDD) dataset are presented to demonstrate the flexibility and generalization ability of our HCI. The significant improvements offered by HCI over main-stream detectors such as YOLOX, Faster RCNN, SSD, and EfficientDet also highlight the effectiveness of our approach for night-time vehicle detection.