DETEKSI PENGGUNAAN HELM SAFETY PADA PEKERJA MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE VERSI 8 (YOLOV8)

An increase in workplace accidents by 5% in Indonesia in 2021 underscores the urgency of implementing a strong Occupational Safety and Health (OSH) culture, including the use of Personal Protective Equipment (PPE) such as construction helmets. However, the low education level among workers (57.5%) poses a challenge in raising awareness about the importance of OSH. To address this issue, this study utilizes deep learning-based image processing technology with the YOLOv8 algorithm to detect helmet usage by workers in real-time. The model was trained using a dataset containing 654 images of workers obtained from Roboflow. The training results showed robust performance, with a reduction in loss value and an improvement in accuracy based on key metrics such as precision, recall, and mean Average Precision (mAP). YOLOv8, with its anchor-free technique and high efficiency, successfully detected helmets with a confidence level of over 90%. The real-time detection capability of YOLOv8 enables continuous safety monitoring at project sites, thereby reducing the risk of accidents due to non-compliance with safety protocols. Additionally, the lightweight nature of YOLOv8 allows its implementation on edge devices, making it a cost-effective and scalable solution for industrial applications. This implementation demonstrates that YOLOv8 is a reliable, efficient, and practical method for enhancing workplace safety by automating PPE monitoring in construction and industrial environments. Furthermore, the use of this technology can assist supervisors in enforcing safety policies, reducing human errors in monitoring, and increasing overall compliance. The integration of AI-based safety monitoring systems such as YOLOv8 has the potential to revolutionize workplace safety standards, making construction sites safer and more efficient. ABSTRAK Peningkatan kecelakaan kerja sebesar 5% di Indonesia pada tahun 2021 menekankan urgensi penerapan budaya Keselamatan dan Kesehatan Kerja (K3) yang kuat, termasuk penggunaan Alat Pelindung Diri (APD) seperti helm proyek. Namun, rendahnya tingkat pendidikan pekerja (57,5%) menjadi tantangan dalam meningkatkan kesadaran akan pentingnya K3. Untuk mengatasi masalah ini, penelitian ini menggunakan teknologi pemrosesan citra berbasis deep learning dengan algoritma YOLOv8 untuk mendeteksi penggunaan helm oleh pekerja secara real-time. Model ini dilatih menggunakan dataset berisi 654 gambar pekerja yang diperoleh dari Roboflow.Hasil pelatihan menunjukkan kinerja yang kuat, dengan penurunan nilai loss serta peningkatan akurasi berdasarkan metrik utama seperti presisi, recall, dan mean Average Precision (mAP). YOLOv8, dengan teknik anchor-free dan efisiensinya yang tinggi, berhasil mendeteksi helm dengan tingkat kepercayaan lebih dari 90%. Kemampuan deteksi real-time YOLOv8 memungkinkan pemantauan keselamatan yang berkelanjutan di lokasi proyek, sehingga dapat mengurangi risiko kecelakaan akibat ketidakpatuhan terhadap protokol keselamatan. Selain itu, sifat YOLOv8 yang ringan memungkinkan penerapannya pada perangkat edge, menjadikannya solusi yang hemat biaya dan skalabel untuk aplikasi industri. Implementasi ini membuktikan bahwa YOLOv8 adalah metode algoritma yang andal, efisien, dan praktis dalam meningkatkan keselamatan kerja dengan mengotomatiskan pemantauan APD di lingkungan konstruksi dan industri.Lebih lanjut, penggunaan teknologi ini dapat membantu pengawas dalam menegakkan kebijakan keselamatan, mengurangi kesalahan manusia dalam pemantauan, serta meningkatkan kepatuhan secara keseluruhan. Integrasi sistem pemantauan keselamatan berbasis AI seperti YOLOv8 berpotensi merevolusi standar keselamatan kerja, menjadikan lokasi konstruksi lebih aman dan lebih efisienPeningkatan kecelakaan kerja sebesar 5% di Indonesia pada tahun 2021 menekankan urgensi penerapan budaya Keselamatan dan Kesehatan Kerja (K3) yang kuat, termasuk penggunaan Alat Pelindung Diri (APD) seperti helm proyek. Namun, rendahnya tingkat pendidikan pekerja (57,5%) menjadi tantangan dalam meningkatkan kesadaran akan pentingnya K3. Untuk mengatasi masalah ini, penelitian ini menggunakan teknologi pemrosesan citra berbasis deep learning dengan algoritma YOLOv8 untuk mendeteksi penggunaan helm oleh pekerja secara real-time. Model ini dilatih menggunakan dataset berisi 654 gambar pekerja yang diperoleh dari Roboflow.Hasil pelatihan menunjukkan kinerja yang kuat, dengan penurunan nilai loss serta peningkatan akurasi berdasarkan metrik utama seperti presisi, recall, dan mean Average Precision (mAP). YOLOv8, dengan teknik anchor-free dan efisiensinya yang tinggi, berhasil mendeteksi helm dengan tingkat kepercayaan lebih dari 90%. Kemampuan deteksi real-time YOLOv8 memungkinkan pemantauan keselamatan yang berkelanjutan di lokasi proyek, sehingga dapat mengurangi risiko kecelakaan akibat ketidakpatuhan terhadap protokol keselamatan. Selain itu, sifat YOLOv8 yang ringan memungkinkan penerapannya pada perangkat edge, menjadikannya solusi yang hemat biaya dan skalabel untuk aplikasi industri. Implementasi ini membuktikan bahwa YOLOv8 adalah alat yang andal, efisien, dan praktis dalam meningkatkan keselamatan kerja dengan mengotomatiskan pemantauan APD di lingkungan konstruksi dan industri.Lebih lanjut, penggunaan teknologi ini dapat membantu pengawas dalam menegakkan kebijakan keselamatan, mengurangi kesalahan manusia dalam pemantauan, serta meningkatkan kepatuhan secara keseluruhan. Integrasi sistem pemantauan keselamatan berbasis AI seperti YOLOv8 berpotensi merevolusi standar keselamatan kerja, menjadikan lokasi konstruksi lebih aman dan lebih efisien

YOLOT: A Recurrent YOLO Model for Robust Video-Based Automotive Object Detection

Though incredibly effective at detecting objects in isolated frames, modern object detection models are often not designed to take advantage of information present in previous frames of a video stream, despite that data being readily avail- able. To address this shortcoming, this paper proposes YOLOT, a modification of the widely used YOLOv8 object detection model, which seeks to utilize this temporal information with the addition of recurrent structures. In the design of YOLOT, a series of recurrent convolutional modules were inserted at backbone and neck outputs and the final and most effective design was found to be the insertion of a Convolutional Gated Recurrent Unit before the detect heads of the model. Training and evaluation of YOLOT are both performed on the challenging BDD100k MOTS Dataset, a benchmark for automotive object detection across seven classes. When evaluated, YOLOT outperforms the baseline architecture of YOLOv8 on the BDD100k validation dataset by 8.2 points in mAP50, while adding 10ms of inference time on an Nvidia RTX 3060m GPU. In addition to the development of YOLOT, this paper serves as the most comprehensive docu- ment outlining YOLOv8 and YOLOT’s architecture to date, including a detailed description of the baseline model and loss function, from basic concepts to high level design

Pengembangan Model Klasifikasi Kendaraan Keluar Masuk Area Parkir Dengan Algoritma YOLOv8

Peningkatan laju pertumbuhan mahasiswa baru menimbulkan tantangan serius terhadap infrastruktur parkir di Universitas Muhammadiyah Kalimantan Timur (UMKT). Data terkini menunjukkan adanya peningkatan signifikan sekitar 10% dari tahun sebelumnya, mencapai 2.598 mahasiswa baru pada tahun 2022. Ruang lingkup penelitian ini adalah melakukan proses klasifikasi kendaraan tetapi tidak melakukan tracking kendaraan, data yang digunakan adalah data dari perekaman video yang dilakukan pada simpang tanjakan menuju area parkir kampus bagian atas di siang hari, serta objek yang dideteksi adalah motor, mobil dan manusia, sedangkan yang dihitung keluar masuknya adalah mobil dan motor. Tujuan penelitian ini adalah mengimplementasikan algoritma YOLOv8 agar dapat mendeteksi serta mengklasifikasikan kendaraan keluar masuk area parkir serta untuk mengetahui bagaimana proses deteksi dapat diterapkan agar dapat akurat untuk mendeteksi kendaraan yang keluar masuk area parkir. Metode penelitian melibatkan pengumpulan data dan penerapan algoritma YOLOv8 (You Only Look Once) untuk training dan validasi model pada platform Google Colab yang mendukung GPU untuk mempercepat komputasi dan memungkinkan pengolahan data dalam skala besar. Hasil dari penelitian ini adalah model klasifikasi yang dapat mendeteksi kendaraan keluar masuk area parkir UMKT dengan memiliki nilai mAP50 sebesar 89,8% dan nilai presisi sebesar 86,5%. Penelitian selanjutnya diharapkan dapat mengembangkan model dengan tingkat akurasi yang lebih tinggi dengan mengintegrasikan CCTV sebagai sumber video secara real-time

Soybean seedling detection and counting from UAV images based on an improved YOLOv8 Network

The utilization of unmanned aerial vehicle (UAV) for soybean seedling detection is an effective way to estimate soybean yield, which plays a crucial role in agricultural planning and decision-making. However, the soybean seedlings objects in the UAV image are small, in clusters, and occluded each other, which makes it very challenging to achieve accurate object detection and counting. To address these issues, we optimize the YOLOv8 model and propose a GAS-YOLOv8 network, aiming to enhance the detection accuracy for the task of soybean seedling detection based on UAV images. Firstly, a global attention mechanism (GAM) is incorporated into the neck module of YOLOv8, which reallocates weights and prioritizes global information to more effectively extract soybean seedling features. Secondly, the CIOU loss function is replaced with the SIOU loss, which includes an angle loss term to guide the regression of bounding boxes. Experimental results show that, on the soybean seedling dataset, the proposed GAS-YOLOv8 model achieves a 1.3% improvement in [email protected] and a 6% enhancement in detection performance in dense seedling areas, when compared to the baseline model YOLOv8s.When compared to other object detection models (YOLOv5, Faster R-CNN, etc.), the GAS-YOLOv8 model similarly achieved the best detection performance. These results demonstrate the effectiveness of the GAS-YOLOv8 in detecting dense soybean seedlings, providing more accurate theoretical support for subsequent yield estimation

Evaluating Fine Tuned Deep Learning Models for Real-Time Earthquake Damage Assessment with Drone-Based Images

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the feld of earthquake damage detection by (1) demonstrating the efectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes

Instance Segmentation and Teeth Classification in Panoramic X-rays

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3\% increase in mAP score for teeth classification compared to existing methods, and a 10-15\% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis.Comment: submtted to Expert Systems with Applications Journa

Research on YOLOv8 algorithm for pulmonary nodule detection in CT images

Pulmonary nodules remain an important radiological marker for early lung cancer and require their accurate detection for early screening and diagnosis. However, because they are small-sized structures that have poor visual contrast and tend to resemble surrounding tissues, modern detection algorithms often find it hard to correctly identify these small lesions. For improved detection accuracy, therefore, this work presents an improved model based on YOLOv8 as its backbone framework. The major improvement is achieved through incorporation of Res2Net as a feature extraction path, thus increasing the network ability to map out multi-scale information and its sensitivity to small nodule structures. Res2Net differs from typical convolutional blocks because it takes a complex approach to grouping and fusion over different scales within each residual block. With Res2Net included as YOLOv8's backbone, its upgraded version further improves recognition accuracy for small-sized targets and ill-defined boundaries while retaining an over-all lightweight structure. The model is trained and validated on CT images processed through standardization and lung parenchyma extraction using the LUNA16 database. Test results show that the upgraded version outperforms the original YOLOv8 in both accuracy and computational efficiency. Additionally, an evaluation is made using alternative prominent detection networks used in practice, including YOLOv5, YOLOv6, and YOLOv3-Tiny. Results show that the upgraded model outcompetes its alternatives in detection efficiency while retaining low computational requirements and shows strong applicability for real-world applications

Real Time Vessel Detection Model Using Deep Learning Algorithms for Controlling a Barrier System

[Abstract]: This study addresses marine pollution caused by debris entering the ocean through rivers. A physical and bubble barrier system has been developed to collect debris, but an effective identification and classification system for incoming vessels is needed. This study evaluates the effectiveness of deep learning models in identifying and classifying vessels in real time. The YOLO (You Only Look Once) v5 and v8 models are evaluated for vessel detection and classification. A dataset of 624 images representing 13 different types of vessels was created to train the models. The YOLOv8, featuring a new backbone network, outperformed the YOLOv5 model, achieving a high mean average precision (mAP@50) of 98.9% and an F1 score of 91.6%. However, YOLOv8’s GPU consumption increased by 116% compared to YOLOv5. The advantage of the proposed method is evident in the precision–confidence curve (PCC), where the accuracy peaks at 1.00 and 0.937 confidence, and in the achieved frames per second (fps) value of 84.7. These findings have significant implications for the development and deployment of real-time marine pollution control technologies. This study demonstrates that YOLOv8, with its advanced backbone network, significantly improves vessel detection and classification performance over YOLOv5, albeit with higher GPU consumption. The high accuracy and efficiency of YOLOv8 make it a promising candidate for integration into marine pollution control systems, enabling real-time identification and monitoring of vessels. This advancement is crucial for enhancing the effectiveness of debris collection systems and mitigating marine pollution, highlighting the potential for deep learning models to contribute to environmental preservation efforts.This research was funding by CT Engineers with the grant number INV0022

Deep Learning-based Weapon Detection using Yolov8

Deep learning (DL), a subset of machine learning (ML), has demonstrated remarkable success in image recognition and object detection tasks. This study presents a deep learning-based approach for offline weapon detection using the YOLOv8m architecture. A custom YOLO-formatted dataset was developed, comprising over 10,000 annotated images spanning two weapon categories: guns (all types of firearms) and knives (all types). The model achieved a Mean Average Precision ([email protected]) of 0.852. and [email protected]:0.95 of 0.622, with precision and recall scores of 0.89 and 0.80, respectively. The class-wise evaluation revealed strong detection across both weapons, with [email protected] of 0.871 for knives and 0.831 for guns. Despite occasional false positives and class confusion, the system shows promise for offline weapon detection tasks