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

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 introducing 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.