Research on road object detection algorithm based on improved YOLOX

TL;DR

This paper enhances the YOLOX object detection algorithm for road safety by introducing new loss functions and dynamic anchor mechanisms, significantly improving detection accuracy for small and occluded objects in autonomous driving.

Contribution

It proposes DecIoU boundary box loss, Push Loss, and dynamic anchor box mechanism to improve detection of occluded and small objects in road scenes.

Findings

Achieved 88.9% mAP and 91.0% mAR on KITTI dataset with YOLOX-s.

Improved YOLOX-m achieved 89.1% mAP and 91.4% mAR, surpassing baseline performance.

Demonstrated effectiveness of the proposed methods through extensive experiments.

Abstract

Road object detection is an important branch of automatic driving technology, The model with higher detection accuracy is more conducive to the safe driving of vehicles. In road object detection, the omission of small objects and occluded objects is an important problem. therefore, reducing the missed rate of the object is of great significance for safe driving. In the work of this paper, based on the YOLOX object detection algorithm to improve, proposes DecIoU boundary box regression loss function to improve the shape consistency of the predicted and real box, and Push Loss is introduced to further optimize the boundary box regression loss function, in order to detect more occluded objects. In addition, the dynamic anchor box mechanism is also used to improve the accuracy of the confidence label, improve the label inaccuracy of object detection model without anchor box. A large number of experiments on KITTI dataset demonstrate the effectiveness of the proposed method, the improved YOLOX-s achieved 88.9% mAP and 91.0% mAR on the KITTI dataset, compared to the baseline version improvements of 2.77% and 4.24%; the improved YOLOX-m achieved 89.1% mAP and 91.4% mAR, compared to the baseline version improvements of 2.30% and 4.10%.