Woraratpanya, Kuntpong

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Woraratpanya, Kuntpong
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Weraratpanya, Kuntpong
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kuntpong.wo@kmitl.ac.th

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    Evaluation of deep learning algorithms for semantic segmentation of car parts
    (2022-10-01)
    Pasupa, Kitsuchart
    ;
    Kittiworapanya, Phongsathorn
    ;
    Hongngern, Napasin
    ;
    Woraratpanya, Kuntpong
    Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = 69.6 % when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.
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    Local Sigmoid Method: Non-Iterative Deterministic Learning Algorithm for Automatic Model Construction of Neural Network
    (2020-01-01)
    Alfarozi, Syukron Abu Ishaq
    ;
    Pasupa, Kitsuchart
    ;
    Sugimoto, Masanori
    ;
    Woraratpanya, Kuntpong
    A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a randomization technique that requires a large number of hidden nodes to achieve the high accuracy. This leads to a large and complex model, which is slow at the inference time. Previously, we reported analytical incremental learning (AIL) algorithm, which is a compact model and a non-iterative deterministic learning algorithm, to be used as an alternative. However, AIL cannot grow its set of hidden nodes, due to the node saturation problem. Here, we describe a local sigmoid method (LSM) that is also a sufficiently compact model and a non-iterative deterministic learning algorithm to overcome both the ELM randomization and AIL node saturation problems. The LSM algorithm is based on 'divide and conquer' method that divides the dataset into several subsets which are easier to optimize separately. Each subset can be associated with a local segment represented as a hidden node that preserves local information of the subset. This technique helps us to understand the function of each hidden node of the network built. Moreover, we can use such a technique to explain the function of hidden nodes learned by backpropagation, the iterative algorithm. Based on our experimental results, LSM is more accurate than other non-iterative learning algorithms and one of the most compact models.
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    Enhancing Thai Food Recognition Through Multimodal Fusion of Image and Fourier Spectrum
    (2024-01-01)
    Pasupa, Kitsuchart
    ;
    Woraratpanya, Kuntpong
    Recognizing food is a challenging task in artificial intelligence research because food items may undergo deformations during cooking or serving, and they can be partially or fully occluded, making it difficult for recognition systems to analyze their complete visual information. Therefore, in addition to evaluating object detection effectiveness, consideration must be given to the texture of the food. However, Convolutional Neural Networks may fall short in capturing textural information. In this research, we propose a method to enhance the efficiency of Thai food recognition by employing the concept of multi-modal fusion, incorporating Fourier Spectrum images to take texture representation into account and improve the model’s performance. In the fusion process, we employed the CentralNet framework and compared it with baselines (using only images and conventional concatenation fusion) on three datasets: THFOOD-50, FoodyDudy, and our newly proposed food dataset called iTHFOOD-200. This new dataset encompasses a more diverse range of food types. The experimental results demonstrate that fusion with the CentralNet framework yielded better performance than the baselines using only images (7.1% Top-1 Accuracy and 3.9% Top-5 Accuracy on average) and conventional concatenation fusion (5.0% Top-1 Accuracy and 2.4% Top-5 Accuracy on average).