Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

Duong, Tung-Lam; Le, Van-Duc; Bui, Tien-Cuong; To, Hai-Thien

doi:10.1007/978-3-031-25082-8_11

Cited by 2 publications

(1 citation statement)

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“…Their approach contains several steps: detect cars in subsequent parking lot images with YOLO for approximately five minutes and extract bounding box centers; cluster and eliminate false detection bounding box centers with a clustering algorithm, and from cluster centers, detect bounding boxes of parking spaces; and perform the classification for detected parking spaces. Duong et al [40] introduced an object detector for parking occupancy detection, OcpDet, based on RetinaNet, with its backbone, ResNet, replaced by MobileNet. They mainly emphasize scalability and reliability.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Parking Lot Occupancy Detection with Improved MobileNetV3

Yuldashev,

Mukhiddinov,

Abdusalomov

et al. 2023

Sensors

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In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT datasets. The model processes individual parking space patches from real-time video feeds, providing occupancy classification for each patch, identifying occupied or available spaces. Our architectural modifications include the integration of a convolutional block attention mechanism in place of the native attention module and the adoption of blueprint separable convolutions instead of the traditional depth-wise separable convolutions. In terms of performance, our proposed model exhibits superior results when benchmarked against state-of-the-art methods. Achieving an exceptional area under the ROC curve (AUC) value of 0.99 for most experiments with the PKLot dataset, our enhanced MobileNetV3 showcases its exceptional discriminatory power in binary classification. Benchmarked against the CarNet and mAlexNet models, representative of previous state-of-the-art solutions, our proposed model showcases exceptional performance. During evaluations using the combined CNRPark-EXT and PKLot datasets, the proposed model attains an impressive average accuracy of 98.01%, while CarNet achieves 97.03%. Beyond achieving high accuracy and precision comparable to previous models, the proposed model exhibits promise for real-time applications. This work contributes to the advancement of parking lot occupancy detection by offering a robust and efficient solution with implications for urban mobility enhancement and resource optimization.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%