Research on a multi-scale traffic scene object detection algorithm based on Light-YOLOX

Abstract

A multi-scale traffic scene object detection algorithm based on Light-YOLOX has been proposed with the aim of achieving a substantial enhancement in accuracy while maintaining a lightweight design. To accomplish this, the algorithm has undergone further refinement by introduc`ng a pyramidal attention segmentation module in the backbone network and pooling structure. This enhancement has significantly improved the algorithm ' s capacity to extract contextual information at various scales, leading to more precise and comprehensive object detection in traffic scenes. Moreover, the development of the Py-FPN feature fusion structure, achieved through the integration of multi-scale pyramidal convolution, has enabled the complete fusion of output feature layers from the backbone network. This integration has further heightened the algorithm's detection effectiveness, ensuring a more robust and accurate detection of objects within traffic scenes. Through experimental evaluations on both the KITTI dataset and Cityscapes dataset, it has been demonstrated that the proposed multi-scale traffic scene object detection method delivers a considerable improvement in accuracy and comprehensive performance, despite having a minimal number of parameters and operations. These findings underscore the algorithm ' s potential applicability and effectiveness in the practical field of traffic scene object detection, illustrating its suitability for real-world deployment and utilization.