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%.Comment: 9 pages; in Chines