What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector

• URL: http://arxiv.org/abs/2409.07813v1
• Date: Thu, 12 Sep 2024 07:46:58 GMT
• Title: What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector
• Authors: Muhammad Yaseen,
• Abstract summary: This study focuses on the YOLOv9 object detection model, focusing on its architectural innovations, training methodologies, and performance improvements. Key advancements, such as the Generalized Efficient Layer Aggregation Network GELAN and Programmable Gradient Information PGI, significantly enhance feature extraction and gradient flow. This paper provides the first in depth exploration of YOLOv9s internal features and their real world applicability, establishing it as a state of the art solution for real time object detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study provides a comprehensive analysis of the YOLOv9 object detection model, focusing on its architectural innovations, training methodologies, and performance improvements over its predecessors. Key advancements, such as the Generalized Efficient Layer Aggregation Network GELAN and Programmable Gradient Information PGI, significantly enhance feature extraction and gradient flow, leading to improved accuracy and efficiency. By incorporating Depthwise Convolutions and the lightweight C3Ghost architecture, YOLOv9 reduces computational complexity while maintaining high precision. Benchmark tests on Microsoft COCO demonstrate its superior mean Average Precision mAP and faster inference times, outperforming YOLOv8 across multiple metrics. The model versatility is highlighted by its seamless deployment across various hardware platforms, from edge devices to high performance GPUs, with built in support for PyTorch and TensorRT integration. This paper provides the first in depth exploration of YOLOv9s internal features and their real world applicability, establishing it as a state of the art solution for real time object detection across industries, from IoT devices to large scale industrial applications.

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• YOLOv11: An Overview of the Key Architectural Enhancements [0.5639904484784127]
  The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB) We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.
  arXiv  Detail & Related papers  (2024-10-23T09:55:22Z)
• What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector [0.0]
  This study presents a detailed analysis of the YOLOv8 object detection model. It focuses on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5. The paper reviews YOLOv8's performance across benchmarks like Microsoft COCO and Roboflow 100, highlighting its high accuracy and real-time capabilities.
  arXiv  Detail & Related papers  (2024-08-28T15:18:46Z)
• 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)
• LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection [63.780355815743135]
  We present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder.
  arXiv  Detail & Related papers  (2024-06-05T17:07:24Z)
• Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer [0.0]
  This study zeroes in on optimizing the YOLOv7 algorithm to boost its operational efficiency and speed on mobile platforms. The experimental outcomes reveal that the refined YOLO model demonstrates exceptional performance.
  arXiv  Detail & Related papers  (2024-03-04T05:29:32Z)
• SATAY: A Streaming Architecture Toolflow for Accelerating YOLO Models on FPGA Devices [48.47320494918925]
  This work tackles the challenges of deploying stateof-the-art object detection models onto FPGA devices for ultralow latency applications. We employ a streaming architecture design for our YOLO accelerators, implementing the complete model on-chip in a deeply pipelined fashion. We introduce novel hardware components to support the operations of YOLO models in a dataflow manner, and off-chip memory buffering to address the limited on-chip memory resources.
  arXiv  Detail & Related papers  (2023-09-04T13:15:01Z)
• 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)
• EDN: Salient Object Detection via Extremely-Downsampled Network [66.38046176176017]
  We introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image. Experiments demonstrate that EDN achieves sArt performance with real-time speed.
  arXiv  Detail & Related papers  (2020-12-24T04:23:48Z)

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.