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    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.
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    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.
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    Unified histogram equalization for defect detection on air bearing surfaces
    (2017-02-01)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    Delivery of zero-defect products to customers in due time is key to customer satisfaction. This paper presents a new machine vision system for detecting the defects on the air bearing surface of the head gimbal assembly (HGA). The paper presents two contributions: a practical software implementation by using unified histogram equalization, and a defect detection algorithm with a block matrix technique and texture analysis. In order to test the algorithm with a real-time system, a high speed capsule conveyor was built as a new, fast in-line conveyor for transporting capsules containing HGAs. According to the experimental results, the defect detection was drastically enhanced and the performance of the proposed algorithm was satisfactory for use in a real assembly line. In other words, the visual subsystem was successful at capturing moving parts during image acquisition and at equalizing the acquired image. This new system can be used to replace a slow-speed detection system in order to increase the unit per hour production of an industrial assembly line.
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    A practical low-cost machine vision sensor system for defect classification on air bearing surfaces
    (2017-01-01)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    In this paper, we present a newly adapted machine vision method and a practical low-cost machine vision sensor for defect classification of the air bearing surfaces (ABSs) of a hard disk drive, which controls the flying height of the recording heads moving above a disk in operation. A defective ABS can cause poor reading and writing performance; hence, it is necessary to verify its integrity before assembling it into the final product. The proposed sensor system was designed and implemented to detect defects by an effective combination of image segmentation and block matrix techniques as well as classifying them using an expert system under dark- and bright-field conditions. Our system processes subregions of interest and sub-blocks in parallel so that they can take advantage of multiple processor cores. From the trial runs, the small fractional error and low average processing time suggested that our proposed system is effective and can be used in an industrial assembly line.
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    Volume estimation of symmetrical object using laser light sectioning
    (2014-01-01)
    Gulphanich, Suphan
    ;
    Songthai, Maethinee
    ;
    Suesut, Taweepol
    This paper presents a method to estimate volume from three-dimensional characteristics of symmetrical objects by computer vision system using laser light sectioning. The camera captures the image of laser line projected on the object moving on a conveyor. The captured images can be estimated the widths and heights at various locations along the object. The area of cross section can be integrated to be volume of the object. The image data is extracted from the structured laser light projecting on the surface. After image pre-processing, the laser profile is calculated the center of gravity in order to extract the image coordinate. The homography transformation is used to transform the extracted coordinate to the real world distance (in metric system). A metric vision based on light sectioning for estimating the volume of symmetrical shapes (rectangular shape and circular shape) of the object were tested. The accuracy of estimation can apply in the automatic size grading on the conveyer as well.