{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:21:09Z","timestamp":1784737269539,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nantong Basic Science Research Program","award":["JC2023021"],"award-info":[{"award-number":["JC2023021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s25020534","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T11:24:56Z","timestamp":1737113096000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety"],"prefix":"10.3390","volume":"25","author":[{"given":"Jincheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menglin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyang","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Engineering Training Center, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2019). 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