Computer Vision Research Articles

Light-Adapted Optoelectronic-Memristive Device for the Artificial Visual System.

With the development of artificial intelligence systems, it is necessary to develop optoelectronic devices with photoresponse and storage capacity to simulate human visual perception systems. The key to an artificial visual perception system is to integrate components with both sensing and storage capabilities of illumination information. Although module integration components have made useful progress, they still face challenges such as multispectral response and high energy consumption. Here, we developed a light-adapted optoelectronic-memristive device integrated by an organic photodetector and ferroelectric-based memristor to simulate human visual perception. ITO/P3HT:PC71BM/Au as the light sensor unit shows a high on/off ratio (Iph/Id) reaching ∼5 × 104 at 0 V. The memristor unit, consisting of ITO/CBI@P(VDF-TrFE)/Cu, has a RON/ROFF ratio window of ∼106 under 0.05 V read voltage and ultralow power consumption of ∼1 pW. Moreover, the artificial visual perception unit shows stable light-adapted memory windows under different wavelengths of irradiation light (400, 500, and 600 nm; they meet the spectral range of human visual recognition) and can clearly identify the target image ("T" shape) because of the apparent contrast, which results from the high ROFF/RON ratio values. These results provide a potential design strategy for the development of intelligent artificial vision systems.

The design of a new interactive multimedia system based on computer vision and multi-sensing techniques for the traditional ritual process

ABSTRACT With the rapid advancements in technology, digital media have become widely integrated into people’s lives. In oriental countries, individuals frequently visit temples for fortune-telling and blessing-praying during the ritual process. A new interactive multimedia system based on computer vision and multi-sensing techniques for carrying out the traditional ritual process in the temple is proposed, which contributes to cultural preservation. The three major ritual steps, namely, ‘incense waving,’ ‘moon-block casting,’ and ‘fortune-telling poem drawing,’ were implemented by multimedia-related hardware and algorithms, and the system was exhibited for field testing by 405 participants. Interviews with experts and a questionnaire survey of the participants’ opinions were conducted to evaluate the effectiveness of the system using statistical software packages, yielding the following findings: (1) the proposed system can bring the participants to experience the ritual process effectively; (2) the proposed three major ritual steps not only attracts more participations but also promotes people’s learning of the ritual process; (3) the system attracts participants by integrating multimedia effects with good usability; and (4) the uses of computer vision and multi-sensing techniques bring the users new experiences of the fortune-telling application.

Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes.

The ability to recover tissue deformation from visual features is fundamental for many robotic surgery applications. This has been a long-standing research topic in computer vision, however, is still unsolved due to complex dynamics of soft tissues when being manipulated by surgical instruments. The ambiguous pixel correspondence caused by homogeneous texture makes achieving dense and accurate tissue tracking even more challenging. In this paper, we propose a novel self-supervised framework to recover tissue deformations from stereo surgical videos. Our approach integrates semantics, cross-frame motion flow, and long-range temporal dependencies to enable the recovered deformations to represent actual tissue dynamics. Moreover, we incorporate diffeomorphic mapping to regularize the warping field to be physically realistic. To comprehensively evaluate our method, we collected stereo surgical video clips containing three types of tissue manipulation (i.e., pushing, dissection and retraction) from two different types of surgeries (i.e., hemicolectomy and mesorectal excision). Our method has achieved impressive results in capturing deformation in 3D mesh, and generalized well across manipulations and surgeries. It also outperforms current state-of-the-art methods on non-rigid registration and optical flow estimation. To the best of our knowledge, this is the first work on self-supervised learning for dense tissue deformation modeling from stereo surgical videos. Our code will be released.

AI-based methods for detecting and classifying age-related macular degeneration: a comprehensive review

This paper explores the advancements and achievements of artificial intelligence (AI) in computer vision (CV), particularly in the context of diagnosing and grading age-related macular degeneration (AMD), one of the most common leading causes of blindness and low vision that impact millions of patients globally. Integrating AI in biomedical engineering and healthcare has significantly enhanced the understanding and development of the CV application to mimic human problem-solving abilities. By leveraging AI-based models, ophthalmologists can improve the accuracy and speed of disease diagnosis, enabling early treatment and mitigating the severity of the conditions. This paper presents a comprehensive analysis of many studies on AMD published between 2014 and 2024, with more than 80% published after 2020. Various methodologies and techniques are examined, particularly emphasizing utilizing different retinal imaging modalities like color fundus photography and optical coherence tomography (OCT), where 66% of the studies used OCT datasets. This review aims to compare the efficacy of these AI-based approaches, including machine learning and deep learning, in detecting and diagnosing different stages and grades of AMD based on the evaluation of different performance metrics using different private and public datasets. In addition, this paper introduces some suggested AI solutions for future work.

Evaluating Activation Functions in GAN Models for Virtual Inpainting: A Path to Architectural Heritage Restoration

Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inpainting to reconstruct missing architectonical elements in images of ruined Greek temples to measure the performance of different activation functions. Unlike a previous study related to this work, a direct reconstruction process without segmented images was used. Then, two evaluation methods are presented: the objective one (mathematical metrics) and an expert (visual perception) evaluation to measure the performance of the different approaches. Results conclude that ReLU outperforms other activation functions, while Mish and Leaky ReLU perform poorly, and Swish’s professional evaluations highlight a gap between mathematical metrics and human visual perception.

Recognizing Handwritten Digits on MNIST Dataset using KNN Algorithm

Handwritten digit recognition is a critical job in computer vision and is used as a frequent benchmark for testing machine learning algorithms. This work describes the creation of a recognition system utilizing the K-Nearest Neighbors (KNN) method, which was chosen for its simplicity and ease of understanding. The system is built on the MNIST dataset, which contains a vast number of photographs of handwritten digits. The process begins with data collection and investigation, which involves analyzing the content and properties of the MNIST dataset to better comprehend the range of handwriting styles. This is followed by data preparation, which consists of standardizing pixel values to increase algorithm performance and dividing the dataset into training and testing sets. This split aids in assessing the model's generalization capabilities. KNN was selected as the model due to its non-parametric nature, meaning it does not make assumptions about the data's underlying distribution. This characteristic makes it versatile and effective for classification tasks like digit recognition. The model is trained in a unique way compared to other machine learning methods: instead of learning parameters, KNN stores all the training data and uses it during the prediction phase to classify new data points based on the nearest neighbors. The performance of the model was evaluated using accuracy as a metric, which measures the percentage of correctly classified digits. Different values of kkk, the number of neighbors, were tested to find the optimal setting. The results showed that KNN could effectively classify handwritten digits with significant accuracy. Finally, the paper discusses future work and deployment considerations. It highlights the potential for deploying KNN-based digit recognition systems in real-world applications and suggests further research to refine preprocessing techniques, optimize model parameters, and explore more advanced models, such as neural networks, to enhance accuracy and efficiency. This study demonstrates the practical applicability of KNN in digit recognition and outlines a path for future improvements.

STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting

As a renewable and clean energy source, wind energy has caught worldwide attention. To ensure the reliability and stability of wind energy production, wind speed forecasting is of great importance. Although existing forecasting models have shown promising results, they remain relatively small in scale and are incapable of learning comprehensive sequential representations from large-scale data to reason intricate spatio-temporal patterns obscured in entangled wind series. Recently, pre-trained large models have demonstrated impressive performance in natural language processing (NLP) and computer vision (CV). However, large models are rarely applied to wind speed forecasting due to the lack of large-scale wind data for training. To resolve this issue, we propose Spatio-Temporal Enhanced Pre-Trained Large Language Model, namely STELLM, innovatively leveraging the powerful reasoning ability of Large Language Models (LLMs) for accurate wind speed forecasting. Specifically, we first perform the series decomposition to separate wind series data into seasonal and trend components, capturing the distinct properties from disentangled patterns. Then, to empower LLMs with the spatio-temporal modeling capability, we introduce the spatial and temporal prompts, which fuse the geographic distribution information and short-term temporal patterns of wind turbines to enrich the input wind series. Moreover, we leverage an autoregressive fine-tuning strategy to align LLMs with wind series data and learn patch-level representations. Afterwards these representations are fed into a localized spatial module to capture the spatial dependencies between wind turbines. Extensive experiments on four public wind datasets demonstrate the performance superiority of STELLM.

A Lightweight Double Compression Detector for HEIF Images Based on Encoding Information

Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid’s work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image’s quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy.

Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis.

Recent advancements in artificial intelligence (AI) and large language models (LLMs) have shown potential in medical fields, including dermatology. With the introduction of image analysis capabilities in LLMs, their application in dermatological diagnostics has garnered significant interest. These capabilities are enabled by the integration of computer vision techniques into the underlying architecture of LLMs. This study aimed to compare the diagnostic performance of Claude 3 Opus and ChatGPT with GPT-4 in analyzing dermoscopic images for melanoma detection, providing insights into their strengths and limitations. We randomly selected 100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. Images were included if they were dermoscopic images of melanocytic lesions with histopathologically confirmed diagnoses. Each model was given the same prompt, instructing it to provide the top 3 differential diagnoses for each image, ranked by likelihood. Primary diagnosis accuracy, accuracy of the top 3 differential diagnoses, and malignancy discrimination ability were assessed. The McNemar test was chosen to compare the diagnostic performance of the 2 models, as it is suitable for analyzing paired nominal data. In the primary diagnosis, Claude 3 Opus achieved 54.9% sensitivity (95% CI 44.08%-65.37%), 57.14% specificity (95% CI 46.31%-67.46%), and 56% accuracy (95% CI 46.22%-65.42%), while ChatGPT demonstrated 56.86% sensitivity (95% CI 45.99%-67.21%), 38.78% specificity (95% CI 28.77%-49.59%), and 48% accuracy (95% CI 38.37%-57.75%). The McNemar test showed no significant difference between the 2 models (P=.17). For the top 3 differential diagnoses, Claude 3 Opus and ChatGPT included the correct diagnosis in 76% (95% CI 66.33%-83.77%) and 78% (95% CI 68.46%-85.45%) of cases, respectively. The McNemar test showed no significant difference (P=.56). In malignancy discrimination, Claude 3 Opus outperformed ChatGPT with 47.06% sensitivity, 81.63% specificity, and 64% accuracy, compared to 45.1%, 42.86%, and 44%, respectively. The McNemar test showed a significant difference (P<.001). Claude 3 Opus had an odds ratio of 3.951 (95% CI 1.685-9.263) in discriminating malignancy, while ChatGPT-4 had an odds ratio of 0.616 (95% CI 0.297-1.278). Our study highlights the potential of LLMs in assisting dermatologists but also reveals their limitations. Both models made errors in diagnosing melanoma and benign lesions. These findings underscore the need for developing robust, transparent, and clinically validated AI models through collaborative efforts between AI researchers, dermatologists, and other health care professionals. While AI can provide valuable insights, it cannot yet replace the expertise of trained clinicians.

Detection and Tracking of Underwater Fish Using the Fair Multi-Object Tracking Model: A Comparative Analysis of YOLOv5s and DLA-34 Backbone Models

Modern aquaculture utilizes computer vision technology to analyze underwater images of fish, contributing to optimized water quality and improved production efficiency. The purpose of this study is to efficiently perform underwater fish detection and tracking using multi-object tracking (MOT) technology. To achieve this, the FairMOT model was employed to simultaneously implement pixel-level object detection and re-identification (Re-ID) functions, comparing two backbone models: FairMOT+YOLOv5s and FairMOT+DLA-34. The study constructed a dataset targeting the popular black porgy in Korean aquaculture, using underwater video data from five different environments collected from the internet. During the training process, the FairMOT+YOLOv5s model rapidly reduced train loss and demonstrated stable performance. The FairMOT+DLA-34 model showed better results in ID tracking performance, with an accuracy of 44.1%, an IDF1 of 11.0%, an MOTP of 0.393, and an IDSW of 1. In contrast, the FairMOT+YOLOv5s model recorded an accuracy of 43.8%, an IDF1 of 14.6%, an MOTP of 0.400, and an IDSW of 10. The results of this study indicate that the FairMOT+YOLOv5s model demonstrated higher IDF1 and MOTP scores compared to the FairMOT+DLA-34 model, while the FairMOT+DLA-34 model showed superior performance in ID tracking accuracy and had fewer ID switches.

MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation

Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep learning frameworks demonstrate significant advantages in segmentation accuracy. However, existing deep learning-based superpixel algorithms suffer from a loss of details due to convolution and upsampling operations in their encoder–decoder structure, which weakens their semantic detection capabilities. To overcome these limitations, we propose a novel superpixel segmentation network based on a multi-attention hybrid network (MAS-Net). MAS-Net is still based on an efficient symmetric encoder–decoder architecture. First, utilizing residual structure based on a parameter-free attention module at the feature encoding stage enhanced the capture of fine-grained features. Second, adoption of a global semantic fusion self-attention module was used at the feature selection stage to reconstruct the feature map. Finally, fusing the channel with the spatial attention mechanism at the feature-decoding stage was undertaken to obtain superpixel segmentation results with enhanced boundary adherence. Experimental results on real-world image datasets demonstrated that the proposed method achieved competitive results in terms of visual quality and metrics, such as ASA and BR-BP, compared with the state-of-the-art approaches.

High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery

Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.

Identification of psychological stress from speech signal using deep learning algorithm

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events with the help of external action units is one of the research subjects to explore various human behavior and their dependencies. Existing work applied various deep learning algorithms to outline the correlation between psychological activities with human emotions. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for a period of time and various interrogation sessions to finalize the emotional severity of an individual. Few skilled specialists and the lack of medical knowledge of human emotions drive the need for computer vision approaches to emphasize emotion recognition, particularly in disorders. The proposed study specifically assesses the use of speech signals to identify psychological disorders in terms of stress characteristics and uses a deep learning model that incorporates feed-forward networks and long short-term memory (LSTM) to identify the degree of psychological disease. The study made use of a standard speech dataset that was gathered from a variety of patients using a standard questionnaire format. The survey was conducted throughout a few Indian states. Speech samples were taken from patients whose cortisol levels were higher than 10 %. To assess the relationship between speech and psychological activity, speech signals from each patient have been gathered. The spectrogram of the speech signal's Mel filter bank coefficients has been analyzed, and the characteristics that cause stress and those that don't have it have been further divided into categories. The suggested model classifies stress and non-stress features in 150 voice dataset subjects with an average accuracy of 98 %. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, improves the overall mental healthcare system.

On Developing a Machine Learning-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance

The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively.

On the benefit of attention in inverse design of thin films filters

Abstract Attention layers are a crucial component in many modern deep learning models, particularly those used in natural language processing and computer vision. Attention layers have been shown to improve the accuracy and effectiveness of various tasks, such as machine translation, image captioning, etc. Here, the benefit of attention layers in designing optical filters based on a stack of thin film materials is investigated. The superiority of Attention layers over fully-connected Deep Neural Networks is demonstrated for this task.

Improved Arithmetic Optimization Algorithm with Transfer Learning based Arabic Sign Language Identification System

The Arabic sign language (ArSL) has witnessed ground-breaking research activities to identify hand gestures and signs through the deep learning (DL) model. SL is a unique communication tool that bridges the gaps between people with hearing impairment and ordinary people. The ArSL recognition system is of immense importance for different groups of people since it enables individuals with hearing impairment to communicate effectively. In SLs, signs are characterized by discrepancies in hand positions, shapes, motions, body parts, and facial expressions, posing a crucial threat to visual recognition in computer vision (CV). An automated sign detection technique needs two primary courses of action: the recognition of specific features and the classification of the input dataset. Previously, several approaches for detecting and classifying SLs had been proposed. In this study, an Improved Metaheuristics with Transfer Learning based Arabic Sign Language Identification System (IMTL-ArSL) method is developed. The primary objective of the IMTL-ArSL method is to detect and classify the existence of various signs in the Arabic language. In the IMTL-ArSL model, the bilateral filtering (BF) model is initially used for preprocessing. Besides, the Residual Network (ResNet50v2) model is applied for extracting features, and an improved arithmetic optimization algorithm (IAOA) model is utilized for hyperparameter tuning. Finally, a gated recurrent unit (GRU) network is exploited to identify sign languages. The empirical analysis of the IMTL-ArSL approach is tested using a benchmark dataset. The experimental values of the IMTL-ArSL approach portrayed a superior accuracy value of 93.87% over other techniques.

A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy.

Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models

Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions. A key contribution of our work is the generation of additional labels from the CelebA dataset, categorizing images into three groups based on prevalent illumination direction: left, center, and right. These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations. Leveraging a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), our method achieves noteworthy manipulations, encompassing a wide rotation angle of ±30°. while preserving individual distinct characteristics even under challenging illumination conditions. Our methodology involves computing trajectories that approximate clouds of latent representations of dataset samples with different yaw rotations through linear regression. Specific trajectories are obtained by analyzing subsets of data that share significant attributes with the source image, including light direction. Notably, our approach does not require any specific training of the generative model for the task of rotation; we merely compute and follow specific trajectories in the latent space of a pre-trained face generation model. This article showcases the potential of our approach and its current limitations through a qualitative discussion of notable examples. This study contributes to the ongoing advancements in representation learning and the semantic investigation of the latent space of generative models.

Menumbuhkan Literasi Teknologi Melalui Pengenalan Aplikasi Computer Vision Di Kalangan Pelajar

The purpose of this community service project is to provide education to enhance technological literacy by introducing the basics of Computer Vision applications among students. The activities included a seminar attended by 170 students. An analysis using the Wilcoxon statistical test on pre-post test results showed a significant improvement in participants' understanding of Computer Vision applications. The test results indicated a significant difference before and after the activity with a value of 0.011. Through this community service, participants have successfully grasped the material presented to enhance literacy as change agents in the digital era, positively impacting societal progress through improved understanding of technology in the field of Computer Vision.

Assessing CNN Architectures for Estimating Correct Posture in Cruise Machinists

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries.