Optimizing YOLOv8 for Parking Space Detection: Comparative Analysis of Custom YOLOv8 Architecture

• URL: http://arxiv.org/abs/2505.17364v1
• Date: Fri, 23 May 2025 00:36:50 GMT
• Title: Optimizing YOLOv8 for Parking Space Detection: Comparative Analysis of Custom YOLOv8 Architecture
• Authors: Apar Pokhrel, Gia Dao,
• Abstract summary: Parking space occupancy detection is a critical component in the development of intelligent parking management systems.<n>Traditional object detection approaches, such as YOLOv8, provide fast and accurate vehicle detection across parking lots.<n>We perform a comprehensive comparative analysis of customized backbone architectures integrated with YOLOv8.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parking space occupancy detection is a critical component in the development of intelligent parking management systems. Traditional object detection approaches, such as YOLOv8, provide fast and accurate vehicle detection across parking lots but can struggle with borderline cases, such as partially visible vehicles, small vehicles (e.g., motorcycles), and poor lighting conditions. In this work, we perform a comprehensive comparative analysis of customized backbone architectures integrated with YOLOv8. Specifically, we evaluate various backbones -- ResNet-18, VGG16, EfficientNetV2, Ghost -- on the PKLot dataset in terms of detection accuracy and computational efficiency. Experimental results highlight each architecture's strengths and trade-offs, providing insight into selecting suitable models for parking occupancy.

Related papers

• YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries [0.0]
  This paper introduces YOLO-APD, a novel deep learning architecture enhancing the YOLOv8 framework specifically for this challenge.<n>YOLO-APD achieves state-of-the-art detection accuracy, reaching 77.7% mAP@0.5:0.95 and exceptional pedestrian recall exceeding 96%.<n>It maintains real-time processing capabilities at 100 FPS, showcasing a superior balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-07-07T18:03:40Z)
• YOLOv12: A Breakdown of the Key Architectural Features [0.5639904484784127]
  YOLOv12 is a significant advancement in single-stage, real-time object detection.<n>It incorporates an optimised backbone (R-ELAN), 7x7 separable convolutions, and FlashAttention-driven area-based attention.<n>It offers scalable solutions for both latency-sensitive and high-accuracy applications.
  arXiv  Detail & Related papers  (2025-02-20T17:08:43Z)
• Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 [1.9926435972281176]
  This work introduces a novel approach that integrates Internet of Things, Edge Computing, and Deep Learning concepts, by using the latest YOLO models for vehicle detection.<n>A new pixel-wise post-processing ROI selection method is proposed for accurately identifying regions of interest to count vehicles in parking lot images.<n>The proposed system achieved 99.68% balanced accuracy on a custom dataset of 3,484 images, offering a cost-effective smart parking solution.
  arXiv  Detail & Related papers  (2024-12-02T21:24:31Z)
• 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)
• Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking [55.13878429987136]
  We propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets. Our method has achieved significant improvements on MOT17 and MOT20 datasets while reaching state-of-the-art performance on DanceTrack dataset.
  arXiv  Detail & Related papers  (2023-11-17T08:17:49Z)
• SEPT: Towards Efficient Scene Representation Learning for Motion Prediction [19.111948522155004]
  This paper presents SEPT, a modeling framework that leverages self-supervised learning to develop powerful models for complex traffic scenes. experiments demonstrate that SEPT, without elaborate architectural design or feature engineering, achieves state-of-the-art performance on the Argoverse 1 and Argoverse 2 motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2023-09-26T21:56:03Z)
• The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset [120.08736654413637]
  The quality of the features extracted by the backbone architecture can have a significant impact on the overall detection performance. Our study evaluates three well-known autonomous vehicle datasets, namely KITTI, NuScenes, and BDD, to compare the performance of different backbone architectures on object detection tasks.
  arXiv  Detail & Related papers  (2023-09-15T17:32:15Z)
• 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)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots [65.33650222396078]
  We develop a parking lot environment and collect a dataset of human parking maneuvers. We compare a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment
  arXiv  Detail & Related papers  (2020-04-21T20:46:32Z)
• VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification [116.1587709521173]
  We propose to build a large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets. We design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet. We achieve the state-of-art accuracy of 86.07% mAP on the private test set of AICity Challenge.
  arXiv  Detail & Related papers  (2020-04-14T05:06:38Z)

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.