Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors

• URL: http://arxiv.org/abs/2411.00201v1
• Date: Thu, 31 Oct 2024 20:45:00 GMT
• Title: Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors
• Authors: Nidhal Jegham, Chan Young Koh, Marwan Abdelatti, Abdeltawab Hendawi,
• Abstract summary: This study presents a benchmark analysis of various YOLO (You Only Look Once) algorithms, from YOLOv3 to the newest addition, YOLO11. It evaluates their performance on three diverse datasets: Traffic Signs (with varying object sizes), African Wildlife (with diverse aspect ratios and at least one instance of the object per image), and Ships and Vessels (with small-sized objects of a single class)
• Score: 0.0
• License:
• Abstract: This study presents a comprehensive benchmark analysis of various YOLO (You Only Look Once) algorithms, from YOLOv3 to the newest addition. It represents the first research to comprehensively evaluate the performance of YOLO11, the latest addition to the YOLO family. It evaluates their performance on three diverse datasets: Traffic Signs (with varying object sizes), African Wildlife (with diverse aspect ratios and at least one instance of the object per image), and Ships and Vessels (with small-sized objects of a single class), ensuring a comprehensive assessment across datasets with distinct challenges. To ensure a robust evaluation, we employ a comprehensive set of metrics, including Precision, Recall, Mean Average Precision (mAP), Processing Time, GFLOPs count, and Model Size. Our analysis highlights the distinctive strengths and limitations of each YOLO version. For example: YOLOv9 demonstrates substantial accuracy but struggles with detecting small objects and efficiency whereas YOLOv10 exhibits relatively lower accuracy due to architectural choices that affect its performance in overlapping object detection but excels in speed and efficiency. Additionally, the YOLO11 family consistently shows superior performance in terms of accuracy, speed, computational efficiency, and model size. YOLO11m achieved a remarkable balance of accuracy and efficiency, scoring mAP50-95 scores of 0.795, 0.81, and 0.325 on the Traffic Signs, African Wildlife, and Ships datasets, respectively, while maintaining an average inference time of 2.4ms, a model size of 38.8Mb, and around 67.6 GFLOPs on average. These results provide critical insights for both industry and academia, facilitating the selection of the most suitable YOLO algorithm for diverse applications and guiding future enhancements.

Related papers

• YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning [0.4143603294943439]
  Method for 3D pose estimation of immature green apples (fruitlets) in commercial orchards was developed. YOLO11 object detection and pose estimation algorithm alongside Vision Transformers (ViT) for depth estimation. YOLO11n surpassed all configurations of YOLO11 and YOLOv8 in terms of box precision and pose precision.
  arXiv  Detail & Related papers  (2024-10-21T17:00:03Z)
• Quantizing YOLOv7: A Comprehensive Study [0.0]
  This paper studies the effectiveness of a variety of quantization schemes on the pre-trained weights of the state-of-the-art YOLOv7 model. Results show that using 4-bit quantization coupled with the combination of different granularities results in 3.92x and 3.86x memory-saving for uniform and non-uniform quantization.
  arXiv  Detail & Related papers  (2024-07-06T03:23:04Z)
• YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision [0.6662800021628277]
  This paper focuses on the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance improvements, and suitability for edge deployment across these versions.
  arXiv  Detail & Related papers  (2024-07-03T10:40:20Z)
• YOLOv10: Real-Time End-to-End Object Detection [68.28699631793967]
  YOLOs have emerged as the predominant paradigm in the field of real-time object detection. The reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs. We introduce the holistic efficiency-accuracy driven model design strategy for YOLOs.
  arXiv  Detail & Related papers  (2024-05-23T11:44:29Z)
• YOLO-World: Real-Time Open-Vocabulary Object Detection [87.08732047660058]
  We introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed.
  arXiv  Detail & Related papers  (2024-01-30T18:59:38Z)
• YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection [80.11152626362109]
  We provide an efficient and performant object detector, termed YOLO-MS. We train our YOLO-MS on the MS COCO dataset from scratch without relying on any other large-scale datasets. Our work can also be used as a plug-and-play module for other YOLO models.
  arXiv  Detail & Related papers  (2023-08-10T10:12:27Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• An Informative Tracking Benchmark [133.0931262969931]
  We develop a small and informative tracking benchmark (ITB) with 7% out of 1.2 M frames of existing and newly collected datasets. We select the most informative sequences from existing benchmarks taking into account 1) challenging level, 2) discriminative strength, 3) and density of appearance variations. By analyzing the results of 15 state-of-the-art trackers re-trained on the same data, we determine the effective methods for robust tracking under each scenario.
  arXiv  Detail & Related papers  (2021-12-13T07:56:16Z)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.