{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/revising-deep-learning-methods-in-parking-lot","title":"Revising deep learning methods in parking lot occupancy detection","arxiv_id":"2306.04288","date":"2023-06-07","proceeding":null,"authors":["Anastasia Martynova","Mikhail Kuznetsov","Vadim Porvatov","Vladislav Tishin","Andrey Kuznetsov","Natalia Semenova","Ksenia Kuznetsova"],"abstract":"Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing drivers to search for available parking lots across regions of interest. The classic approach to this task is based on the application of neural network classifiers to camera records. However, existing systems demonstrate a lack of generalization ability and appropriate testing regarding specific visual conditions. In this study, we extensively evaluate state-of-the-art parking lot occupancy detection algorithms, compare their prediction quality with the recently emerged vision transformers, and propose a new pipeline based on EfficientNet architecture. Performed computational experiments have demonstrated the performance increase in the case of our model, which was evaluated on 5 different datasets.","url_abs":"https://arxiv.org/abs/2306.04288v3","url_pdf":"https://arxiv.org/pdf/2306.04288v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"revising-deep-learning-methods-in-parking-lot","repo_url":"https://github.com/eighonet/parking-research","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"parking-space-occupancy","task_name":"Parking Space Occupancy"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"}],"datasets_introduced":[{"slug":"spkl","name":"SPKL","full_name":"Seasonal Parking Lot Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/parking-space-occupancy-on-acmps","task":"Parking Space Occupancy","dataset":"ACMPS","model":"EfficientNet-P","rank_in_archive_order":1,"of":5,"metrics":{"F1-score":"0.9982"},"uses_additional_data":false},{"leaderboard":"/sota/parking-space-occupancy-on-acmps","task":"Parking Space Occupancy","dataset":"ACMPS","model":"MobileNetV2","rank_in_archive_order":2,"of":5,"metrics":{"F1-score":"0.9971"},"uses_additional_data":false},{"leaderboard":"/sota/parking-space-occupancy-on-acmps","task":"Parking Space Occupancy","dataset":"ACMPS","model":"CarNet","rank_in_archive_order":3,"of":5,"metrics":{"F1-score":"0.9877"},"uses_additional_data":false},{"leaderboard":"/sota/parking-space-occupancy-on-acmps","task":"Parking Space 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