International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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FISHEYE IMAGE OBJECT DETECTION : A REVIEW OF IMPROVED YOLO ALGORITHMS AND APPLICATIONS

Author(s) Mr. PRANEETH N, Mr. RISHI R, Mr. HARRSHA R, Prof. Dr. PRASHANT P PATAVARDHAN
Country India
Abstract Fisheye cameras are widely used in autonomous driving, traffic surveillance, parking assistance and indoor monitoring because they can capture a very wide field of view with a single lens. However, the strong radial distortion in fisheye images makes object detection much more difficult than in normal pinhole images. Traditional detectors trained on regular datasets often fail near the image borders, where objects look stretched, curved, or very small. In recent years, many researchers have proposed improved versions of YOLO and other deep learning models to solve these issues and to increase accuracy and robustness in fisheye scenarios. This paper presents a review of such methods, including attention mechanisms, contrastive learning, distortion-aware feature extraction and new bounding box designs. Important fisheye datasets like WoodScape and FishEye8K are discussed, along with benchmark results, evaluation metrics and open challenges. The aim is to give students and beginner researchers a simple, clear view of how improved YOLO-based approaches work for fisheye images, what performance they achieve and where more work is still needed.
Keywords Fisheye camera; Object detection; YOLO; Wide-angle vision; Autonomous driving; Deep learning
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-04
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.67518
Short DOI https://doi.org/hbnpxh
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