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To address this issue, we propose a near obstacle detection method named NOD-AVM (near object detection based on around view monitoring), which utilizes the four wide-angle cameras of the AVM. From the four wide-angle cameras of the AVM, a total of four NOD-AVMs were developed, whose sensing areas are the intersections of two adjacent cameras. In the context of NOD-AVMs, the application of the inverse perspective mapping (IPM) is used to project images from adjacent cameras onto the ground plane. By analyzing the difference between the two adjacent IPM images, the system can ascertain the presence of obstacles on the ground plane. Once an obstacle is detected, the IPM image also allows us to estimate the distance with respect to the ego-vehicle. To validate the feasibility and effectiveness of the proposed NOD-AVM, we have conducted experiments using real-world data collected by a prototype intelligent vehicle in both campus and urban road environments. Experimental results demonstrate that the proposed method can efficiently detect both static and dynamic obstacles near the ego-vehicle and accurately locate them. Dataset and code were uploaded as Support information.<\/jats:p>","DOI":"10.1371\/journal.pone.0336851","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T19:00:22Z","timestamp":1767639622000},"page":"e0336851","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Near obstacles detection by inverse perspective mapping of AVM for intelligent vehicles"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9699-2598","authenticated-orcid":true,"given":"Zhe","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yule","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minwang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaozheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"issue":"3","key":"pone.0336851.ref001","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/jimaging11030069","article-title":"Improving object detection in high-altitude infrared thermal images using magnitude-based pruning and non-maximum suppression","volume":"11","author":"Y Dash","year":"2025","journal-title":"J Imaging"},{"issue":"1","key":"pone.0336851.ref002","article-title":"Object detection in motion management scenarios based on deep learning","volume":"20","author":"B Pei","year":"2025","journal-title":"PLoS One"},{"issue":"5","key":"pone.0336851.ref003","doi-asserted-by":"crossref","first-page":"3825","DOI":"10.1007\/s10115-025-02375-9","article-title":"Comprehensive review of deep learning-based tiny object detection: challenges, strategies, and future directions","volume":"67","author":"M Muzammul","year":"2025","journal-title":"Knowl Inf Syst"},{"issue":"10","key":"pone.0336851.ref004","article-title":"MonoDCN: Monocular 3D object detection based on dynamic convolution","volume":"17","author":"S Qu","year":"2022","journal-title":"PLoS One"},{"key":"pone.0336851.ref005","doi-asserted-by":"crossref","first-page":"126804","DOI":"10.1016\/j.neucom.2023.126804","article-title":"A systematic literature review on object detection using near infrared and thermal images","volume":"560","author":"N Bustos","year":"2023","journal-title":"Neurocomputing"},{"key":"pone.0336851.ref006","doi-asserted-by":"crossref","unstructured":"Cui H, Li Y, Liu J. 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