# Research on road object detection algorithm based on improved YOLOX

arXiv: https://arxiv.org/abs/2302.08156 · HTML: https://mlsanity.com/paper/2302.08156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [OTA: Optimal Transport Assignment for Object Detection](https://mlsanity.com/paper/2103.14259/) (2021)
- [SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving](https://mlsanity.com/paper/1612.01051/) (2016)
- [NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection](https://mlsanity.com/paper/2106.02426/) (2021)
- [Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations](https://mlsanity.com/paper/1710.02728/) (2017)
- [Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection](https://mlsanity.com/paper/2104.14082/) (2021)

## Abstract (arXiv)

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%.