YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2410.22898v1
• Date: Wed, 30 Oct 2024 10:57:46 GMT
• Title: YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems
• Authors: Mujadded Al Rabbani Alif,
• Abstract summary: This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models. YOLO11 introduces architectural improvements designed to enhance detection speed, accuracy, and robustness in complex environments. We evaluate YOLO11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP)
• Score: 0.0
• License:
• Abstract: Accurate vehicle detection is essential for the development of intelligent transportation systems, autonomous driving, and traffic monitoring. This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models, focusing exclusively on vehicle detection tasks. Building upon the success of its predecessors, YOLO11 introduces architectural improvements designed to enhance detection speed, accuracy, and robustness in complex environments. Using a comprehensive dataset comprising multiple vehicle types-cars, trucks, buses, motorcycles, and bicycles we evaluate YOLO11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLO11 surpasses previous versions (YOLOv8 and YOLOv10) in detecting smaller and more occluded vehicles while maintaining a competitive inference time, making it well-suited for real-time applications. Comparative analysis shows significant improvements in the detection of complex vehicle geometries, further contributing to the development of efficient and scalable vehicle detection systems. This research highlights YOLO11's potential to enhance autonomous vehicle performance and traffic monitoring systems, offering insights for future developments in the field.

Related papers

• YOLO-Vehicle-Pro: A Cloud-Edge Collaborative Framework for Object Detection in Autonomous Driving under Adverse Weather Conditions [8.820126303110545]
  This paper proposes two innovative deep learning models: YOLO-Vehicle and YOLO-Vehicle-Pro. YOLO-Vehicle is an object detection model tailored specifically for autonomous driving scenarios. YOLO-Vehicle-Pro builds upon this foundation by introducing an improved image dehazing algorithm.
  arXiv  Detail & Related papers  (2024-10-23T10:07:13Z)
• YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving [10.103731437332693]
  It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. Existing object detection algorithms can hardly detect these small scaled signs. A YOLO PPA based traffic sign detection algorithm is proposed in this paper.
  arXiv  Detail & Related papers  (2024-09-05T07:49:21Z)
• Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis [0.0]
  This study provides a comparative analysis of five YOLOv5 variants, YOLOv5n6s, YOLOv5s6s, YOLOv5m6s, YOLOv5l6s, and YOLOv5x6s, for vehicle detection. YOLOv5n6s demonstrated a strong balance between precision and recall, particularly in detecting Cars.
  arXiv  Detail & Related papers  (2024-08-22T17:06:29Z)
• YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism [0.0]
  This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems.
  arXiv  Detail & Related papers  (2024-06-17T06:31:43Z)
• 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)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Unsupervised Driving Event Discovery Based on Vehicle CAN-data [62.997667081978825]
  This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner. We evaluate our approach with a dataset of real Tesla Model 3 vehicle CAN-data and a two-hour driving session that we annotated with different driving events.
  arXiv  Detail & Related papers  (2023-01-12T13:10:47Z)
• Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh [0.0]
  We find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh. Models were trained on a dataset containing 7390 images belonging to 21 types of vehicles. We found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.
  arXiv  Detail & Related papers  (2022-12-18T18:53:35Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• 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)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)

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.