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Subject: search.filters.subject.image segmentation

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    Zero-Shot Eggshell Crack Detection Using Grounding DINO and FFT-Based Outer-to-Inner Ring Energy Ratio
    (2025-01-01)
    Soontornnapar, Tomorn
    ;
    Ploysuwan, Tuchsanai
    This study presents a novel approach to eggshell crack detection by integrating zero-shot learning with advanced image analysis techniques. The proposed method utilizes a hybrid dataset composed of a custom hen egg collection-50 tray images containing 30 eggs each arranged in a 6 × 5 grid-and the Botta et al. duck egg dataset, comprising approximately 1,000 images of cracked and intact eggs. Using YOLOv8s and the Fast Segment Anything Model (FastSAM), we generate bounding boxes, annotate segmentation masks, and crop background-free images, resulting in a curated set of 1,500 hen egg images alongside the processed duck egg samples. To localize crack regions accurately, we employ Grounding DINO with fine-grained prompting. To distinguish cracks from dirt and subtle surface irregularities, we introduce a novel frequency-domain feature: the FFT-based Outer-to-Inner (O/I) Ring Energy Ratio, which enhances the visibility of fine anomalies. Our zero-shot detection model achieves an average accuracy of 92.54% across 10-fold cross-validation on both datasets-without retraining-demonstrating strong generalization and eliminating the reliance on labeled training data.We benchmark our approach against supervised models (CNN, SVM, XGBoost, k-NN) and popular zero-shot and anomaly detection frameworks (CLIP, CLIPSeg, Florence-2, and SAA). For real-world validation, we implement our system on an egg-belt conveyor using a low-cost CCD microscope camera capturing 60 FPS video. The model processes one hen egg in tray images at 7.79 FPS in simulation and 0.44 FPS in real-time video testing, enabling inspection of approximately 1,794 eggs per hour per camera. The system's scalability with multiple cameras enables adaptation to industrial egg-sorting speeds. The results demonstrate significant advancements in crack detection accuracy and throughput, highlighting the potential of combining frequency-based image analysis with zero-shot prompting for scalable, real-time quality control in the poultry industry. The full implementation and associated dataset are publicly available at (https://github.com/tomorn112/ZC-DINO-ER/).
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    PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads
    (2025-01-01)
    Punyammaree, Chaiwat
    ;
    Kaitwanidvilai, Somyot
    This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. A U-Net model optimized via Particle Swarm Optimization (PSO) is developed to enhance segmentation performance by automatically tuning hyperparameters. The methodology includes optimized SEM image acquisition, preprocessing (patch-based subdivision and expert annotation), PSO-driven hyperparameter selection, and post-processing with thresholding and connected component analysis for pit counting. Experimental results demonstrate that the PSO-optimized U-Net significantly outperforms standard U-Net, SegNet, and LinkNet models, achieving an F1-score of 79.60%, an IoU of 86.51%, and an accuracy of 99.77%. Additionally, the proposed method achieves 86.9% counting accuracy, surpassing human experts (72.7%) while processing images 15 times faster (180 seconds vs. 2700 seconds per image). These findings highlight the potential of PSO-optimized deep learning for improving HDD quality control by providing an accurate, efficient, and standardized solution for corrosion pit detection, ultimately reducing the risk of HDD failure and data loss.
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    Multihead Attention U-Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation
    (2024-10-01)
    Juhong, Aniwat
    ;
    Li, Bo
    ;
    Liu, Yifan
    ;
    Yang, Chia Wei
    ;
    Yao, Cheng You
    Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning-based approach for MPI-CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworms (NWs-ICG) as the tracer. The NWs-ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U-Net model to perform segmentation on the MPI-CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI-CT dataset.
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    Modified adaptive thresholding using integral image
    (2016-11-18)
    Peuwnuan, Kittipop
    ;
    Woraratpanya, Kuntpong
    ;
    Pasupa, Kitsuchart
    Adaptive thresholding, the simple way to perform image segmentation, is a form of image thresholding used to classify pixels as dark and light. Taking grayscale image as an input for this task is only good in case that text appears in low intensity area. In case that text appears in high intensity area, it leads to lower recall rate for text detection process. However, it can be fixed by taking an inverted-grayscale image instead. The problem is how to determine automatically whether it is better to take the normal-grayscale or inverted-grayscale image for each original image. This paper proposes the simple way to do that by means of the adaptive thresholding using the integral image itself with some additional steps based on its principle. The proposed method consists of two main process, low/high intensity area segmentation and modified adaptive thresholding.