Object Detection for City and Highway Driving Scenario with YOLOX and Mask RCNN

This paper explores the integration of two deep learning models that are currently being used for object detection, specifically Mask R-CNN and YOLOX, for two distinct driving environments: urban cityscapes and highway settings. The hypothesis underlying this work is that different methods of object detection will work best in different driving environments, due to the differences in their unique strengths as well as the key differences in those driving environments. Some of these differences in the driving environment include varying traffic densities, diverse object classes, and differing scene complexities, including specific differences such as the types of signs present, the presence or absence of stoplights, and the limited-access nature of highways as compared to city streets. As part of this work, a scene classifier has also been developed to categorize the driving context into the two categories of highway and urban driving, in order to allow the overall object detection system to determine which model should be used. This is expected to aid in scene-aware object detection and enhance overall driving safety and efficiency. Through experimentation and evaluation on real-world datasets, the proposed approach demonstrates significant improvements in detection accuracy and computational efficiency compared to previous methods.