arXiv · · Significant research
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
debris classification · construction · Xception · machine learning · waste management
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arXiv ·
This paper surveys machine learning approaches using monument pictures for analyzing heritage sites in India. It addresses challenges in the tourism sector, such as the unavailability of trained personnel and the lack of accurate information. The research aims to provide insights for building an automated decision system to modernize the tourism experience for visitors in India.
arXiv ·
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
arXiv ·
This paper introduces a convolutional transformer model for classifying tomato maturity, along with a new UAE-sourced dataset, KUTomaData, for training segmentation and classification models. The model combines CNNs and transformers and was tested against two public datasets. Results showed state-of-the-art performance, outperforming existing methods by significant margins in mAP scores across all three datasets.
arXiv ·
Researchers at KFUPM have developed a system for pothole detection and characterization using a YOLOv8-seg model and depth estimation. A new dataset of images and depth maps was collected from roads in Al-Khobar, Saudi Arabia. The system combines segmentation and depth data to provide a more comprehensive pothole characterization, enhancing autonomous vehicle navigation and road maintenance.