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%.

翻译：道路目标检测是自动驾驶技术的重要分支，更高的检测精度模型更有利于车辆的安全行驶。在道路目标检测中，小目标和遮挡目标的漏检是一个重要问题，因此降低目标的漏检率对安全驾驶具有重要意义。本文基于YOLOX目标检测算法进行改进，提出DecIoU边界框回归损失函数以提高预测框与真实框的形状一致性，并引入Push Loss进一步优化边界框回归损失函数，以便检测更多遮挡目标。此外，还采用动态锚框机制提高置信度标签的准确性，改善无锚框目标检测模型标签不准确的问题。在KITTI数据集上进行的大量实验验证了所提出方法的有效性，改进的YOLOX-s在KITTI数据集上取得了88.9%的mAP和91.0%的mAR，相比基准版本分别提升了2.77%和4.24%；改进的YOLOX-m取得了89.1%的mAP和91.4%的mAR，相比基准版本分别提升了2.30%和4.10%。