Performance of Recent tiny/small YOLO Versions in the Context of Top-view Fisheye Images - IMT Mines Alès Access content directly
Conference Papers Year : 2022

Performance of Recent tiny/small YOLO Versions in the Context of Top-view Fisheye Images

Benoit Faure
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Olfa Haggui
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Baptiste Magnier

Abstract

With the spreading of the computer vision field, human detection and tracking are problems more relevant than ever. However, due to the complexity of fisheye images, current lightweight detection models show difficulty when processing them. The aim of this article is to compare the performance on fisheye images of current real time detection solutions, specifically YOLOv3-tiny, YOLOv4-tiny and YOLOv5-small. Experiments carried out using a top-view fisheye camera, show faster performance but the very poor detection quality from YOLOv4-tiny. YOLOv5-small, while being slightly slower, gives a far better detection than both other solutions. The database created for this paper is available online. In conclusion, the current review shows YOLOv5-small is the best solution out of the 3 reviewed in a fast, real time, fisheye application.
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Dates and versions

hal-03676679 , version 1 (24-05-2022)

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Benoit Faure, Nathan Odic, Olfa Haggui, Baptiste Magnier. Performance of Recent tiny/small YOLO Versions in the Context of Top-view Fisheye Images. ISHAPE 2022 - 1st International Workshop on Intelligent Systems in Human and Artificial Perception, May 2022, Lecce, Italy. pp.246-257, ⟨10.1007/978-3-031-13321-3_22⟩. ⟨hal-03676679⟩
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