Abandoned-Cart-Vision: Abandoned Cart Detection Using a Deep Object Detection Approach in a Shopping Parking Space

Melegrito, Mark P.; Alon, Alvin Sarraga; Militante, Sammy V.; Austria, Yolanda D.; Polinar, Myriam J.; Mirabueno, Maria Concepcion A.

doi:10.1109/iicaiet51634.2021.9573963

Cited by 24 publications

(1 citation statement)

References 17 publications

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“…Melegrito et al [23] developed a YOLO v3-based transfer learning CNN model to detect abandoned carts, while Manalo et al [24] trained a transfer learning model based on the YOLO v3 framework using the ImageAI python library. These studies demonstrate the application of CNNs and related architectures in various image classification tasks, including crack detection, pothole detection, and object detection in parking lots.…”

Section: B Image Classificationmentioning

confidence: 99%

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Sharif,

Emiola,

Zorto

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring.

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Section: B Image Classificationmentioning

confidence: 99%