KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 17

Some of the metrics are blocked by your
consent settings
YOLO Based IoT Tracking for Academic Labs on Raspberry Pi
(2026-06-16)
Jiamrachada, Tamakorn
;
Nonsiri, Sarayut
;
Kamin, Pichitchai
;
Nanthajirapong, Nathaphon
Academic IoT laboratories often rely on shared equipment, making efficient borrow-return management essential. Conventional management methods depend on manual recording, which can cause verification delays, data entry errors, and increased staff workload. This study proposes a YOLO based IoT equipment tracking system that uses a camera to detect and count devices inside student equipment boxes for borrow-return recording and inventory monitoring. The system runs on a Raspberry Pi 5 for real-time edge-based processing, while detection results are stored in a database and displayed through a web-based dashboard. Experimental results show that the YOLO12n model achieved an F1-score of 0.996 with a real-time inference speed of 12.01 FPS, demonstrating the system's effectiveness in reducing human error and improving laboratory inventory management efficiency.
Some of the metrics are blocked by your
consent settings
YOLO-augment strategy with diffusion-based inpainting for enhanced traffic sign detection
(2026-01-01)
Sub-r-pa, Chayanon
;
Pavarangkoon, Praphan
;
Huang, Su Wen
;
Fan, Ming Zhong
;
Chen, Rung Ching
Traffic sign datasets often suffer from data scarcity and class imbalance, which challenge the development of robust autonomous driving systems. This article proposes a novel dataset augmentation method that leverages Stable Diffusion inpainting to generate realistic synthetic traffic signs. The method fine-tunes a Stable Diffusion model and introduces an object-size-based crop (OSB-crop) technique with mask adjustments to ensure high-quality augmentations that maintain contextual consistency. Evaluations using the Fréchet Inception Distance (FID) show average scores of 195.85 for the DFG-T10 subset and 247.077 for the DFG-B10 subset, demonstrating the ability to produce realistic inpainted signs, particularly for more represented minority classes. Qualitative analyses further highlight seamless integration into real-world scenes, although challenges remain for extremely underrepresented classes and ensuring perfect visual fidelity. The benefits of this approach include its potential to enhance traffic sign datasets, address class imbalances, and improve the potential for training more reliable autonomous driving systems by providing more diverse and realistic training data. This study focuses on evaluating the quality of the generated data itself as a foundational step toward enhancing downstream detection models. However, limitations include the computational cost of fine-tuning and the difficulty in achieving high-quality inpainting for all underrepresented classes, especially those with poor initial data quality. This work lays a strong foundation for advancing dataset augmentation techniques for real-world applications.
Some of the metrics are blocked by your
consent settings
A Low-cost Autonomous Lawn Mower with AI-Based Obstacle Avoidance and GPS Guidance System
(2025-07-01)
Kosri, Thanapon
;
Seekhamharn, Tossawat
;
Phoonsrichaiyasit, Phasawut
;
Khungpo, Poowadon
;
Sirisuk, Phaophak
This paper presents a cost-effective robotic system capable of manual control via RF remote and autonomous navigation using GPS-based information. The system employs artificial intelligence to dynamically classify and avoid non-grass obstacles, ensuring safe operation in real environments. The prototype integrates affordable hardware including Arduino board, sensors, actuators and Raspberry Pi with lightweight algorithms to balance performance and cost. Experimental validation confirms its ability to follow predefined paths with ±1.5 meters deviation in open area and 90% obstacle avoidance success rate. With a total hardware cost under $200, this prototype highlights feasibility for larger-scale implementation.
Some of the metrics are blocked by your
consent settings
Advancing Breast Cancer Identification: Exploring Deep Learning Models for Improved Detection
(2025-01-01)
Samrankit, Thitiphon
;
Surakiat, Kanyanut
;
Tamang, Sudarshan
;
Sharma, Aayushma
;
Paing, May Phu
(1) Background: Breast cancer is the uncontrolled growth of abnormal cells within the breast tissue, and it can be either malignant or benign. Malignant tumors are cancerous and have the potential to invade and spread to other parts of the body while benign tumors are non-cancerous. Traditional breast cancer screening methods have limitations from artifacts and positioning errors. (2) Methods: This study employs various deep learning models—GELAN-C (Generalized Efficient Layer Aggregation Network – Compact version), YOLOv8 (You Only Look Once – version 8), RTMDet (Real-Time Models for Object Detection), DETR (DEtection TRansformer), YOLO-NAS (You Only Look Once – Neural Architecture Search), and Detectron2—to identify the most effective and adaptable model for breast cancer diagnosis. (3) Results: From our experiment, GELAN- C outperformed other models providing exceptional accuracy with the highest mean average precision (mAP) scores of both thresholds. Specifically, GELAN-C achieved a mAP@0.50 of 0.983, which reflects overall object detection accuracy. Additionally, it maintained a strong mAP@0.50-0.95 of 0.868 indicating its robustness and precision across varying levels of detection difficulty.
Some of the metrics are blocked by your
consent settings
2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters
(2025-01-01)
Krungseanmuang, Woranidtha
;
Morita, Fuka
;
Chaowalittawin, Vasutorn
;
Sathaporn, Posathip
;
Kanamori, Chisato
Global climate change impacts all regions and leads to natural disasters such as typhoons, which cause destruction, debris, and flooding. Postdisaster restoration is a very important activity that is mostly done manually and can be time-consuming and challenging, especially in subterranean environments owing to accumulated objects such as pipes, pillars, and mud distributed in confined underground areas. Therefore, in this study, we aim to utilize emerging AI technologies by comparing deep learning algorithms and evaluating four models for 2D object detection and four for 3D point cloud segmentation for detecting sediment accumulation and navigating around obstacles in underground areas after a disaster. Additionally, a custom dataset was developed to simulate underground disaster scenarios. As a result, the You Only Look Once version 11 (YOLOv11) model achieved the highest mean average precision 50 (mAP50: 91.1%) for general detection within the pillar-pipe dataset, whereas the YOLOv12 model performed the best in detecting pipes (mAP50: 87.7%). In the mud dataset, the YOLOv8 segmentation (YOLOv8-seg) model demonstrated superior performance with mAP50 scores of 93.0% (detection) and 86.4% (segmentation). For 3D point cloud segmentation, PointNet achieved the highest accuracy (98.61%), whereas RandLA-Net was optimal for pipe segmentation, achieving an intersection over union score of 37.1%. These findings highlight AI’s potential to accelerate disaster recovery, reduce manual labor, and ensure faster cleanup. Integrating deep learning models into post-typhoon restoration efforts can enable communities to recover more quickly and efficiently after climate change impacts or disaster events.
Some of the metrics are blocked by your
consent settings
The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches
(2024-04-01)
Naing, Kaung Myat
;
Kittichai, Veerayuth
;
Tongloy, Teerawat
;
Chuwongin, Santhad
;
Boonsang, Siridech
Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-of-the-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.
Some of the metrics are blocked by your
consent settings
Reducing the Counting Time of Colonies Using Image Processing Techniques
(2024-01-01)
Siangchin, Apichai
;
Ploysuwan, Tuchsanai
Microbial colony counting is a crucial process in microbiology laboratories and medical research, yet it is often time-consuming and labor-intensive, particularly in large-scale settings or when dealing with numerous samples. Incorporating image processing techniques can significantly reduce the time and error associated with manual colony counting. In this study, we developed and evaluated a deep learning model for automated E. coli colony counting using the YOLO (You Only Look Once) framework. The model achieved an mAP50 of 90.3%, demonstrating its high accuracy and potential for real-world application in laboratories of various sizes. This research not only streamlines the colony counting process but also allows laboratory personnel to allocate their time to other essential tasks.
Some of the metrics are blocked by your
consent settings
BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
(2024-01-01)
Kujareanpaisal, Poonnaphop
;
Mayhasap, Rujira
;
Tea-Makorn, Pin Pin
;
Jindahra, Pavitra
;
Starita, Stefano
The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.
Some of the metrics are blocked by your
consent settings
Weapon Detection in X-ray Image of Baggages
(2024-01-01)
Kundilokovit, Piyapat
;
Thaweechoklertchaikul, Rimthaweep
;
Anuntachai, Anuntapat
Due to the daily commutes of people by MRT trains, following the shooting incident at Paragon, the MRT system has implemented bag searches before entering the stations to look for concealed or hidden weapons. These searches are conducted manually, which sometimes may not be thorough enough and can take a significant amount of time. Especially during peak hours when many people are using the MRT, it is possible for some individuals to pass through the station without being searched. Such actions can render the security measures ineffective. Therefore, this paper proposes a study to find ways to address these issues. From the study and comparison of object detection processes for risky items, such as sharp objects or guns, in X-ray images of luggage, it was found that models such as CNN, RCNN, Detectron, RetinaNet, and Yolo achieved excellent results in object detection and recognition. The organizers plan to apply object detection techniques and improve the existing methods for detecting objects in X-ray images to be more efficient and accurate, capable of identifying a variety of risky items.
Some of the metrics are blocked by your
consent settings
Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm
(2023-03-01)
Kuswantori, Ari
;
Suesut, Taweepol
;
Tangsrirat, Worapong
;
Schleining, Gerhard
;
Nunak, Navaphattra
Featured Application: In the future, the application of this study is very feasible and very close to being implemented for the auto-sorting system for various fish or other objects, in the fish industry or other industries, with deep learning and machine vision technology. Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results