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. 2025 Apr 17;25(8):2518.
doi: 10.3390/s25082518.

RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements

Tianyu Li  1 , Xuanrui Xiong  1 , Yuan Zhang  2 , Xiaolin Fan  1 , Yushu Zhang  1 , Haihong Huang  1 , Dan Hu  1 , Mengting He  1 , Zhanjun Liu  1
Affiliations

RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements

Tianyu Li et al. Sensors (Basel). .

Abstract

Road object detection technology is a key technology to achieve intelligent assisted driving. The complexity and variability of real-world road environments make the detection of densely occluded objects more challenging in autonomous driving scenarios. This study proposes an occluded object detection algorithm, RE-YOLOv5, based on receptive field enhancement to assist with the difficult identification of occluded objects in complex road environments. To efficiently extract irregular features, such as object deformation and truncation in occluded scenes, deformable convolution is employed to enhance the feature extraction network. Additionally, a receptive field enhancement module is designed using atrous convolution to capture multi-scale contextual information and better understand the relationship between occluded objects and their surrounding environment. Considering that the ordinary non-maximum suppression method in dense occlusion scenarios will incorrectly suppress the prediction box of the occluded object, EIOU was used to optimize the non-maximum suppression method. Experiments were conducted on two benchmark datasets, KITTI and CityPersons. The proposed method achieves a mean average precision (mAP) of 82.04% on KITTI, representing an improvement of 2.34% over the baseline model. For heavily occluded objects on CityPersons, the Log Average Miss Rate (MR-2) is reduced to 40.31%, which is a decrease of 9.65% compared to the baseline. These results demonstrate that the proposed method significantly outperforms other comparative algorithms in detecting occluded objects across both datasets.

Keywords: YOLOv5; object detection; occlusion object detection; receptive field.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Model overall structure of RE-YOLOv5.
Figure 2
Figure 2
Schematic diagram of backbone network improvement.
Figure 3
Figure 3
Schematic diagram of RFAE structure.
Figure 4
Figure 4
The schematic diagram of EIOU.
Figure 5
Figure 5
Comparison of KITTI dataset detection results.
Figure 6
Figure 6
Comparison of CityPersons dataset detection results.
Figure 7
Figure 7
Detection results of occluded objects in video data.
Figure 8
Figure 8
Feature heatmap visualization.
See this image and copyright information in PMC

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