Detection parking Spaces by using the AlexNet Algorithm


  • Mustafa J. Hayawi
  • Department of Computer Science, College of Education for Pure Science / University of Thi-Qar


deep learning, convolutional networks, parking lots, occupancy detection, AlexNet architecture


Finding an empty parking space in a crowded area is very stressful due to congestion. In the absence of
empty space, leads to fatigue before the main activity, increased fuel consumption, which leads to
increased pollution, and increased traffic due to the search for empty space. Therefore, it was necessary
to have a system that would help drivers know the condition of each parking space. Empty or occupied
with a vehicle. Where the empty and occupied spaces are calculated and determined so that the system
displays a map of the empty spaces on the screen that is placed in a suitable place in the parking lot to
facilitate directing the driver to the vacant parking spaces and warn if all the parking spaces are
occupied. All this is done through images taken from the surveillance cameras in the parking lot. We find
that the advantages of using vision-based systems over other existing systems are threefold. First There is
no need to update the infrastructure of the parking lot, provided that the place is equipped with
surveillance cameras that monitor the parking spaces covering the entire place. Secondly, camera-based
systems give the exact location i.e. a detailed map of the vacant car parks which are good and necessary
vacant lots. Third, camera-based methods are highly applicable in street parking spaces and residential
areas. Our system achieved excellent results as the average prediction accuracy was 99. 89% and
99.78% on the PKLot data set and the local data set, respectively. This high accuracy and low price makeit a good alternative to systems that use sensors.


H. Ichihashi, A. Notsu, K. Honda, T. Katada, and M. Fujiyoshi, “Vacant parking space detector for

outdoor parking lot by using surveillance camera and FCM classifier,” IEEE Int. Conf. Fuzzy Syst.,

pp. 127–134, 2009, doi: 10.1109/FUZZY.2009.5277099.

D. Bong, K. Ting, and K. Lai, “Integrated approach in the design of car park occupancy

information system (COINS),” IAENG Int. J. Comput. …, no. February, 2008, [Online]. Available:

N. True, “Vacant parking space detection in static images,” Univ. California, San Diego, 2007,

[Online]. Available:

H. Padmasiri, R. Madurawe, C. Abeysinghe, and D. Meedeniya, “Automated Vehicle Parking

Occupancy Detection in Real-Time,” in MERCon 2020 - 6th International Multidisciplinary

Moratuwa Engineering Research Conference, Proceedings, Jul. 2020, pp. 1–6, doi:


P. Hattale, V. Jangam, S. Khilare, Y. Ratnaparkhi, and P. Kasture, “Parking Space Detection Using

Image Processing,” Int. J. Sci. Res., doi: 10.21275/SR21321183644.

D. Acharya and K. Khoshelham, “Real-time image-based parking occupancy detection and

automatic parking slot delineation using deep learning: A tutorial Indoor mapping, modeling and

localization View project Real-time image-based parking occupancy detection and automatic

parking slot deliniation using deep learning: A tutorial.” [Online]. Available:

S. Yamin Siddiqui, M. Adnan Khan, S. Abbas, and F. Khan, “Smart occupancy detection for road

traffic parking using deep extreme learning machine,” J. King Saud Univ. - Comput. Inf. Sci., 2020,

doi: 10.1016/j.jksuci.2020.01.016.

W. Li, L. Cao, L. Yan, C. Li, X. Feng, and P. Zhao, “Vacant parking slot detection in the around

view image based on deep learning,” Sensors (Switzerland), vol. 20, no. 7, Apr. 2020, doi:


A. Farley, H. Ham, and Hendra, “Real Time IP Camera Parking Occupancy Detection using Deep

Learning,” in Procedia Computer Science, 2021, vol. 179, pp. 606–614, doi:


P. Kim, “Matlab deep learning,” With Mach. Learn. neural networks Artif. Intell., vol. 130, no. 21,

S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for

Classifying Images,” Int. J. Sci. Res. Publ., vol. 9, no. 10, p. p9420, 2019, doi:


M. Sewak, M. R. Karim, and P. Pujari, Practical convolutional neural networks: implement

advanced deep learning models using Python. Packt Publishing Ltd, 2018.

M. Z. Alom et al., “The history began from alexnet: A comprehensive survey on deep learning

approaches,” arXiv Prepr. arXiv1803.01164, 2018.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with

Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–

, 2017, doi: 10.1109/TPAMI.2016.2577031.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image

recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

N. A. Muhammad, A. A. Nasir, Z. Ibrahim, and N. Sabri, “Evaluation of CNN, alexnet and

GoogleNet for fruit recognition,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 2, pp. 468–

J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.