Volume V | Issue 4 | 2026 | Paper ID: IJATEM-V05I04P2

Intelligent Parking Space Detection Using Yolov11 with an Enhanced Backbone Network for Accurate Real-Time Vehicle Monitoring

Author(s)

R. Sahila Devi, Vasmitha R I

Cite This

Sahila Devi R., "Intelligent Parking Space Detection Using Yolov11 with an Enhanced Backbone Network for Accurate Real-Time Vehicle Monitoring", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 04, pp. 15-23, 2026.

Abstract

The difficulties of parking management have been aggravated by rapid urbanization and growing number of vehicles, leading to traffic congestion, wastage of fuel and environmental pollution. To address such issues, this paper proposes a Deep Learning (DL) based Parking Space Detection (PSD) framework for accurately detecting free and occupied parking spaces. First, parking lot images are enhanced by using Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, Region of Interest (ROI) is extracted to focus on relevant parking areas. Then, the processed images are classified by a YOLOv11 model with an enhanced backbone network, the EfficientNet-B7, for feature extraction and detection accuracy improvement. The proposed framework integrates the CLAHE, ROI segmentation, and the EfficientNet-B7 enhanced YOLOv11 model for accurate PSD. Experimental results show that the proposed framework achieves 94% mAP50, 91% precision and F1-score of 89%, which are higher than conventional DT, RF, YOLOv5, and Faster R-CNN methods. The proposed system provides reliable real-time monitoring of parking occupancy and enables efficient, intelligent and sustainable smart parking management.

Keywords

CLAHE, ROI, YOLOv11, EfficientNet-B7, DL

References

[1] Artur Janowski; Mustafa Hüsrevoğlu; Malgorzata Renigier-Bilozor, Year: 2024, “Sustainable Parking Space Management Using Machine Learning and Swarm Theory—The SPARK System”, Appl. Sci., Vol: 14, pp. 12076.

CrossRef Google Scholar Publisher Link
[2] Zhuye Xu; Xiao Tang; Changxi Ma; Renshuai Zhang, Year: 2024, “Research on parking space detection and prediction model based on CNN-LSTM”, IEEE Access, Vol: 12, pp. 30085-30100.

CrossRef Google Scholar Publisher Link
[3] Gaurav Satyanath; Jajati Keshari Sahoo; Rajendra Kumar Roul, Year: 2023, “Smart parking space detection under hazy conditions using convolutional neural networks: a novel approach”, Multimedia Tools and Applications Vol: 82, No. 10, pp. 15415-15438.

CrossRef Google Scholar Publisher Link
[4] Chen Huang; Shiyue Yang; Yugong Luo; Yongsheng Wang; Ze Liu, Year: 2022, “Visual detection and image processing of parking space based on deep learning”, Sensors Vol: 22, No: 17, pp. 6672.

CrossRef Google Scholar Publisher Link
[5] Chaojun Wang; Shulin Huang; Cheng Zhang, Year: 2025, “Short-Term Traffic Flow Prediction Considering Weather Factors Based on Optimized Deep Learning Neural Networks: Bo-GRA-CNN-BiLSTM”, Sustainability, Vol: 17, pp. 2576.

CrossRef Google Scholar Publisher Link
[6] Abdullah Alourani; Farzeen Ashfaq; N. Z. Jhanjhi; Navid Ali Khan, Year: 2023, “BiLSTM‐and GNN‐Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data”, Journal of Advanced Transportation, Vol: 2023, No: 1, pp. 8962283.

CrossRef Google Scholar Publisher Link
[7] Wu Wei; Hongyang Chen; Jiayuan Gong; Kai Che; Wenbo Ren; Bin Zhang; Year: 2025, “Real-Time Parking Space Detection Based on Deep Learning and Panoramic Images”, Sensors, Vol: 25, pp. 6449.

CrossRef Google Scholar Publisher Link
[8] Ahmed Mohamed Zaki; S. K. Towfek; Weiguo Gee; Wang Zhang; Marwa Adel Soliman; Year: 2023, “Advancing parking space surveillance using a neural network approach with feature extraction and dipper throated optimization integration”, Journal of Artificial Intelligence and Metaheuristics Vol: 6, No: 2, pp. 16-25.

CrossRef Google Scholar Publisher Link
[9] Issa Dia; Ehsan Ahvar; Gyu Myoung Lee, Year: 2022, “Performance Evaluation of Machine Learning and Neural Network-Based Algorithms for Predicting Segment Availability in AIoT-Based Smart Parking”, Network, Vol: 2, pp. 225-238.

CrossRef Google Scholar Publisher Link
[10] Anchal Dahiya; Pooja Mittal; Yogesh Kumar Sharma; Umesh Kumar Lilhore; Sarita Simaiya; Ehab Ghith; Mehdi Tlija, Year: 2024, “Machine Learning‐Based Prediction of Parking Space Availability in IoT‐Enabled Smart Parking Management Systems”, Journal of Advanced Transportation, Vol: 2024, No: 1, pp. 8474973.

CrossRef Google Scholar Publisher Link
[11] Md Shohel Sayeed; Abdulrahim H; Razak SF; Bukar UA; Yogarayan S, Year: 2023, “IoT Raspberry Pi based smart parking system with weighted K-Nearest Neighbours Approach”, Civil Engineering Journal, vol: 9, No: 8, pp. 1991-2011.

CrossRef Google Scholar Publisher Link
[12] Gaurav Satyanath; Jajati Keshari Sahoo; Rajendra Kumar Roul, Year: 2023 “Smart parking space detection under hazy conditions using convolutional neural networks: a novel approach”, Multimedia Tools and Applications, Vol: 82, No: 10, pp. 15415-15438.

CrossRef Google Scholar Publisher Link
[13] Satar Shaker Muhammad, Year: 2022, “Detection parking Spaces by using the ResNet50 Algorithm”, Journal of Al-Qadisiyah for computer science and mathematics, Vol: 14, No: 2.

CrossRef Google Scholar Publisher Link
[14] Lee Wei Jun; Mohammad Dalvi Esfahani; Tseu Kwan Lee, Year: 2023, “Predicting open parking space using deep learning and support vector regression”, Proceedings of the 2023 7th International Conference on Intelligent Systems”, Metaheuristics & Swarm Intelligence.

CrossRef Google Scholar Publisher Link
[15] Duy-Linh Nguyen; Xuan-Thuy Vo; Adri Priadana; Kang-Hyun Jo, Year: 2024, “Car detection for smart parking systems based on improved YOLOv5”, Vietnam Journal of Computer Science, Vol: 11, No. 02, pp. 195-209.

CrossRef Google Scholar Publisher Link
[16] Chen Huang; Shiyue Yang; Yugong Luo; Yongsheng Wang; Ze Liu, Year: 2022, “Visual Detection and Image Processing of Parking Space Based on Deep Learning”, Sensors, Vol: 22, pp. 6672.

CrossRef Google Scholar Publisher Link

Full Length Article PDF

Article Details