KMITL

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

Browse

Settings

Start date: 2024Subject: search.filters.subject.Machine vision

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp
    (2026-09-01)
    Promnioy, Surasak
    ;
    Phetpan, Kittisak
    ;
    Riza, Dimas Firmanda Al
    ;
    Sharma, Sneha
    ;
    Sirisomboon, Panmanas
    The development of affordable, real-time quality monitoring tools is essential for industrial applications involving high-value tropical fruits such as durian. This study presents a cost-effective short-wave infrared multispectral imaging (SWIR-MSI) system employing three discrete bandpass filters (880, 905, and 940 nm) integrated with machine learning algorithms for non-destructive evaluation of fresh-cut durian pulp. Compared with conventional point-based NIR spectroscopy and complex hyperspectral imaging systems, the proposed configuration markedly reduces system complexity and cost while maintaining sensitivity to key compositional variations. It accurately predicted dry matter content (DMC) and starch, demonstrating that limited spectral information within the 860–1100 nm range can effectively capture moisture- and carbohydrate-related features. These findings confirm the feasibility of implementing filter-based SWIR imaging as a practical and scalable alternative to hyperspectral systems for on-line fruit quality assessment. Practically, this approach enables rapid, non-destructive, and spatially adaptable evaluation of durian pulp quality, offering significant potential for on-site grading, ripeness classification, and process control in fresh-cut durian production and packaging operations, particularly for small- and medium-scale agro-processors.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    FAST DEFECT DETECTION FOR GLASS BOTTLE USING AUTOENCODER AND ERROR THRESHOLD
    (2024-06-01)
    Hanskunatai, Anantaporn
    ;
    Jaiyen, Saichon
    ;
    Claypo, Niphat
    Glass bottle defect detection is an important part of quality control process in any glass manufacturing industry. The bottles must be inspected before packaging. Machine vision for glass bottle defect detection is the technology and method to inspect and analyze the defects for images automatically. Machine vision requires high-ability method to detect the defect and reject the bottle with the defect quickly. In this paper, defect detection framework for glass bottle defect detection tasks using autoencoders and error threshold is proposed. The fast detection method, a small autoencoder neural network architecture was designed with only good bottle images to train an autoencoder neural network. The decoded images are representations of normal bottle images and calculate threshold errors value. Defect detection is done by comparing the error between the normal background image and the encoded images to a threshold error from the training set. The performance of our method was compared to several other methods: VGG16, MobileNetV3, ADA, edge detection and image threshold. The experimental results show that our method yields 80% of accuracy on the body dataset and 92% of accuracy on the neck dataset. The average training time of our method is faster than that of all other neural network-based methods. From the experimental results, we can conclude that our defect detection framework outperforms other approaches both in accuracy and training time for defect detection on the side wall of a glass bottle.