Small Object Detection with YOLO: A Performance Analysis Across Model Versions and Hardware

• URL: http://arxiv.org/abs/2504.09900v1
• Date: Mon, 14 Apr 2025 05:49:31 GMT
• Title: Small Object Detection with YOLO: A Performance Analysis Across Model Versions and Hardware
• Authors: Muhammad Fasih Tariq, Muhammad Azeem Javed,
• Abstract summary: This paper investigates speed and detection accuracy on Intel and CPUs using popular libraries such as ONNX and OpenVINO.<n>We analyze the sensitivity of these YOLO models to object size within the image, examining performance when detecting objects that occupy 1%, 2.5%, and 5% of the total area of the image.
• Score: 2.07180164747172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides an extensive evaluation of YOLO object detection models (v5, v8, v9, v10, v11) by com- paring their performance across various hardware platforms and optimization libraries. Our study investigates inference speed and detection accuracy on Intel and AMD CPUs using popular libraries such as ONNX and OpenVINO, as well as on GPUs through TensorRT and other GPU-optimized frameworks. Furthermore, we analyze the sensitivity of these YOLO models to object size within the image, examining performance when detecting objects that occupy 1%, 2.5%, and 5% of the total area of the image. By identifying the trade-offs in efficiency, accuracy, and object size adaptability, this paper offers insights for optimal model selection based on specific hardware constraints and detection requirements, aiding practitioners in deploying YOLO models effectively for real-world applications.

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