This paper dives into the cutting-edge world of road asset detection on Thai highways, showcasing a novel approach that combines an upgraded REG model with Generalized Focal Loss. Our focus is on identifying key road elements—like pavilions, pedestrian bridges, information and warning signs, and concrete guardrails—to boost road safety and infrastructure management. While deep learning methods have shown promise, traditional models often struggle with accuracy in tricky conditions, such as cluttered backgrounds and variable lighting. To tackle these issues, we’ve integrated REG with Generalized Focal Loss, enhancing its ability to detect road assets with greater precision. Our results are impressive, the REGx model led the way with a mAP50 of 80.340, mAP50-95 of 60.840, precision of 79.100, recall of 76.680, and an F1-score of 77.870. These findings highlight the REGx model’s superior performance, demonstrating the power of advanced deep learning techniques to improve highway safety and infrastructure maintenance, even in challenging conditions.

In this paper, we present MeViT (Medium-Resolution Vision Transformer), designed for semantic segmentation of Landsat satellite imagery, focusing on key economic crops in Thailand para rubber, corn, and pineapple. MeViT enhances Vision Transformers (ViTs) by integrating medium-resolution multi-branch architectures and revising mixed-scale convolutional feedforward networks (MixCFN) to extract multi-scale local information. Extensive experiments on a public Thailand dataset demonstrate that MeViT outperforms state-of-the-art deep learning methods, achieving a precision of 92.22%, recall of 94.69%, F1 score of 93.44%, and mean IoU of 83.63%. These results highlight MeViT’s effectiveness in accurately segmenting Thai Landsat-8 data.

Flooding poses a significant challenge in Thailand due to its complex geography, traditionally addressed through GIS methods like the Flood Risk Assessment Model (FRAM) combined with the Analytical Hierarchy Process (AHP). This study assesses the efficacy of Artificial Neural Networks (ANN) in flood susceptibility mapping, using data from Ayutthaya Province and incorporating 5-fold cross-validation and Stochastic Gradient Descent (SGD) for training. ANN achieved superior performance with precision of 79.90%, recall of 79.04%, F1-score of 79.08%, and accuracy of 79.31%, outperforming the traditional FRAM approach. Notably, ANN identified that only three factors—flow accumulation, elevation, and soil types—were crucial for predicting flood-prone areas. This highlights the potential for ANN to simplify and enhance flood risk assessments. Moreover, the integration of advanced machine learning techniques underscores the evolving capability of AI in addressing complex environmental challenges.

My PhD thesis focuses on improving semantic segmentation of aerial and satellite images, a crucial task for applications like agriculture planning, map updates, route optimization, and navigation. Current models like the Deep Convolutional Encoder-Decoder (DCED) have limitations in accuracy due to their inability to recover low-level features and the scarcity of training data. To address these issues, I propose a new architecture with five key enhancements, a Global Convolutional Network (GCN) for improved feature extraction, channel attention for selecting discriminative features, domain-specific transfer learning to address data scarcity, Feature Fusion (FF) for capturing low-level details, and Depthwise Atrous Convolution (DA) for refining features. Experiments on Landsat-8 datasets and the ISPRS Vaihingen benchmark showed that my proposed architecture significantly outperforms the baseline models in remote sensing imagery.

Colorectal cancer is one of the leading causes of cancer death worldwide. As of now, colonoscopy is the most effective screening tool for diagnosing colorectal cancer by searching for polyps which can develop into colon cancer. The drawback of manual colonoscopy process is its high polyp miss rate. Therefore, polyp detection is a crucial issue in the development of colonoscopy application. Despite having high evaluation scores, the recently published methods based on fully convolutional network (FCN) require a very long inferring (testing) time that cannot be applied in a real clinical process due to a large number of parameters in the network. In this paper, we proposed a compressed fully convolutional network by modifying the FCN-8s network, so our network is able to detect and segment polyp from video images within a real-time constraint in a practical screening routine. Furthermore, our customized loss function allows our network to be more robust when compared to the traditional cross-entropy loss function. The experiment was conducted on CVC-EndoSceneStill database which consists of 912 video frames from 36 patients. Our proposed framework has obtained state-of-the-art results while running more than 7 times faster and requiring fewer weight parameters by more than 9 times. The experimental results convey that our system has the potential to support clinicians during the analysis of colonoscopy video by automatically indicating the suspicious polyps locations.

Semantic segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images has numerous application domains, particularly in road extraction, where the segmented objects serve as essential layers in geospatial databases. Despite several efforts to use deep convolutional neural networks (DCNNs) for road extraction from remote sensing images, accuracy remains a challenge. This paper introduces an enhanced DCNN framework specifically designed for road extraction from remote sensing images by incorporating landscape metrics (LMs) and conditional random fields (CRFs). Our framework employs the exponential linear unit (ELU) activation function to improve the DCNN, leading to a higher quantity and more accurate road extraction. Additionally, to minimize false classifications of road objects, we propose a solution based on the integration of LMs. To further refine the extracted roads, a CRF method is incorporated into our framework. Experiments conducted on Massachusetts road aerial imagery and Thailand Earth Observation System (THEOS) satellite imagery datasets demonstrated that our proposed framework outperforms SegNet, a state-of-the-art object segmentation technique, in most cases regarding precision, recall, and F1 score across various types of remote sensing imagery.

In this paper, we introduce an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Enhancements include the use of ELU (exponential linear unit) instead of ReLU, dataset augmentation with incrementally-rotated images to increase training data by eight times, and the use of landscape metrics to remove false road objects. Tested on the Massachusetts Roads dataset, our method outperformed the SegNet benchmark and other baselines in precision, recall, and F1 scores.