Leveraging Bounding Box Annotations and Boolean Map Saliency for Traffic Light Detection in Foggy Night

Object detection is a fundamental task in computer vision, relying heavily on bounding box (BB) annotations with ground truth labels to train deep learning models. This approach produces impactful results when the boundaries of objects are identifiable, but struggles in adverse weather conditions such as rain, fog, and snow, where object outlines are fuzzy. One such case is the detection of Traffic lights (TLs) in fog at night, as these weather conditions cause the light to scatter in different directions, creating a Halo effect. Therefore, creating BB for manual TL detection annotation is inaccurate. Dense fog makes it difficult for annotators to determine the state (such as red, green, or yellow) and exact location of TLs. Annotation tools are also not designed for blurred images and require annotators to manually adjust parameters like brightness and zoom. To address the challenges of manual annotation, a Boolean Map Saliency (BMS) method is employed to automatically generate annotations that highlight TLs, thereby improving object detection in scenarios where manual BBs are insufficient. Results based on automated BBs and manually annotated bounded boxes are compared using Faster-Rcnn. For both results, the SHIFT dataset is used. Our proposed approach makes it possible to generate superior-quality BBs compared to previous approaches, along with an improved TL detection algorithm, especially on foggy nights when it is difficult for the sensors to detect the TLs.