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. 2025 Jan 17;25(2):534.
doi: 10.3390/s25020534.

PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety

Jincheng Li  1 , Menglin Zheng  1 , Danyang Dong  1 , Xing Xie  2
Affiliations

PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety

Jincheng Li et al. Sensors (Basel). .

Abstract

The issue of obstacle avoidance and safety for visually impaired individuals has been a major topic of research. However, complex street environments still pose significant challenges for blind obstacle detection systems. Existing solutions often fail to provide real-time, accurate obstacle avoidance decisions. In this study, we propose a blind obstacle detection system based on the PC-CS-YOLO model. The system improves the backbone network by adopting the partial convolutional feed-forward network (PCFN) to reduce computational redundancy. Additionally, to enhance the network's robustness in multi-scale feature fusion, we introduce the Cross-Scale Attention Fusion (CSAF) mechanism, which integrates features from different sensory domains to achieve superior performance. Compared to state-of-the-art networks, our system shows improvements of 2.0%, 3.9%, and 1.5% in precision, recall, and mAP50, respectively. When evaluated on a GPU, the inference speed is 20.6 ms, which is 15.3 ms faster than YOLO11, meeting the real-time requirements for blind obstacle avoidance systems.

Keywords: PC-CS-YOLO; YOLO11; deep learning; object detection; visually impaired.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The structural diagram of the PCFN module.
Figure 2
Figure 2
The structural diagram of the CSAF mechanism.
Figure 3
Figure 3
The structural diagram of PC-CS-YOLO.
Figure 4
Figure 4
P-R curve: (a) YOLO11; (b) PC-CS-YOLO.
Figure 5
Figure 5
Confusion matrix: (a) YOLO11; (b) PC-CS-YOLO.
Figure 6
Figure 6
Visualization map: (a–c) raw image; (d–f) YOLO11; (g–i) PC-CS-YOLO.
See this image and copyright information in PMC

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