Ploysuwan, Tuchsanai

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Ploysuwan, Tuchsanai
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tuchsanai.pl@kmitl.ac.th

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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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    Revolutionizing Egg Quality Control: Advanced Prompt-based Models for Automated Detection of Broken Eggs Without the Need for Training
    (2025-02-01)
    Soontornnapar, Tomorn
    ;
    Praneenatthavee, Natdhanai
    ;
    Ploysuwan, Tuchsanai
    This paper proposes an end-to-end pipeline to detect broken eggs in a holder without extensive training, employing a two-step image segmentation and processing approach using saliency scores, all without relying on a large amount of labeled data. The process begins by inputting an egg image with text prompts into Grounding DINO, which returns an egg bounding box. This is followed by the segment anything model (SAM), which extracts the egg’s segmented region. The segmented region is then divided into two crucial components for detection: a binary mask image and a background-removed egg image. The innovation in our method lies in using the saliency score of the estimated anomaly region by employing image processing techniques to effectively distinguish between intact and broken eggs. To validate our approach, we compare it to well-known models such as SVM, XGBoost, and YOLOv8, and we also conduct zero-shot experiments with CLIPSeg, Florence-2, and SAA. In our experimental setup, we utilize 50 egg holder images, each containing both intact and broken eggs. We carefully cropped and processed 30 eggs (arranged in a 6×5 grid) from each holder, resulting in a comprehensive testing dataset totaling 1,500 images. Our results demonstrate the robustness of our method, achieving an impressive 99.56% accuracy in detecting both intact and broken eggs. This breakthrough promises significant advancements in the field of broken egg detection, with broad applications across diverse industries, including food safety, quality control, and automated packaging systems.