PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety
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 avoidanc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/534 |
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| author | Jincheng Li Menglin Zheng Danyang Dong Xing Xie |
| author_facet | Jincheng Li Menglin Zheng Danyang Dong Xing Xie |
| author_sort | Jincheng Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-312b52de3a8d44e89221983fa31856ee |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-312b52de3a8d44e89221983fa31856ee2025-01-24T13:49:16ZengMDPI AGSensors1424-82202025-01-0125253410.3390/s25020534PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired SafetyJincheng Li0Menglin Zheng1Danyang Dong2Xing Xie3School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaSchool of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaSchool of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, ChinaEngineering Training Center, Nantong University, Nantong 226019, ChinaThe 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.https://www.mdpi.com/1424-8220/25/2/534YOLO11deep learningobject detectionvisually impairedPC-CS-YOLO |
| spellingShingle | Jincheng Li Menglin Zheng Danyang Dong Xing Xie PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety Sensors YOLO11 deep learning object detection visually impaired PC-CS-YOLO |
| title | PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety |
| title_full | PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety |
| title_fullStr | PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety |
| title_full_unstemmed | PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety |
| title_short | PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety |
| title_sort | pc cs yolo high precision obstacle detection for visually impaired safety |
| topic | YOLO11 deep learning object detection visually impaired PC-CS-YOLO |
| url | https://www.mdpi.com/1424-8220/25/2/534 |
| work_keys_str_mv | AT jinchengli pccsyolohighprecisionobstacledetectionforvisuallyimpairedsafety AT menglinzheng pccsyolohighprecisionobstacledetectionforvisuallyimpairedsafety AT danyangdong pccsyolohighprecisionobstacledetectionforvisuallyimpairedsafety AT xingxie pccsyolohighprecisionobstacledetectionforvisuallyimpairedsafety |