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Journal articles on the topic 'YOLOv8'

Author: Grafiati

Published: 11 January 2025

Last updated: 31 July 2025

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1

Sharma, Pravek, Dr Rajesh Tyagi, and Dr Priyanka Dubey. "Optimizing Real-Time Object Detection- A Comparison of YOLO Models." International Journal of Innovative Research in Computer Science and Technology 12, no. 3 (2024): 57–74. http://dx.doi.org/10.55524/ijircst.2024.12.3.11.

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Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO varia

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Tahir, Noor Ul Ain, Zhe Long, Zuping Zhang, Muhammad Asim, and Mohammed ELAffendi. "PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8." Drones 8, no. 3 (2024): 84. http://dx.doi.org/10.3390/drones8030084.

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In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feat

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Tresnawati, Dewi, Shopi Nurhidayanti, and Nina Lestari. "A Comparison of YOLOv8 Series Performance in Student Facial Expressions Detection on Online Learning." Jurnal Online Informatika 10, no. 1 (2025): 93–104. https://doi.org/10.15575/join.v10i1.1390.

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Student engagement in online learning is an important factor that can affect learning outcomes. One indicator of engagement is facial expression. However, research on facial expression detection in online learning environments is still limited, especially in the use of the YOLOv8 algorithm. This study aims to compare the performance of several YOLOv8 variants, namely YOLOv8x, YOLOv8m, YOLOv8s, YOLOv8n, and YOLOv8l in recognizing six facial expressions: happy, sad, angry, surprised, afraid, and neutral. Student facial expression data was collected through the Moodle platform every 15 seconds du

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Wulanningrum, Resty, Anik Nur Handayani, and Aji Prasetya Wibawa. "Perbandingan Instance Segmentation Image Pada Yolo8." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (2024): 753–60. http://dx.doi.org/10.25126/jtiik.1148288.

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Seorang pejalan kaki sangat rawan terhadap kecelakaan di jalan. Deteksi pejalan kaki merupakan salah satu cara untuk mengidentifikasi atau megklasifikasikan antara orang, jalan atau yang lainnya. Instance segmentation adalah salah satu proses untuk melakukan segmentasi antara orang dan jalan. Instance segmentation dan penggunaan yolov8 merupakan salah satu implementasi dalam deteksi pejalan kaki. Perbandingan segmentasi pada dataset Penn-Fundan Database menggunakan yolov8 dengan model yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg. Penelitian ini menggunakan dataset publik ped

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Wang, Jiahui, Mengdie Jiang, Tauseef Abbas, Hao Chen, and Yuying Jiang. "YOLOv-MA: A High-Precision Foreign Object Detection Algorithm for Rice." Agriculture 15, no. 13 (2025): 1354. https://doi.org/10.3390/agriculture15131354.

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Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. The proposed algorithm adaptively enhances multi-scale feature representation across small, medium, and large object detection layers by incorporating the multi-scale dilated attention (MSDA) mechanism. Addit

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Weng, Wei-Chun, Chien-Wei Huang, Chang-Chao Su, et al. "Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging." Diagnostics 15, no. 13 (2025): 1686. https://doi.org/10.3390/diagnostics15131686.

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Objective: Esophageal cancer (EC) is difficult to visually identify, rendering early detection crucial to avert the advancement and decline of the patient’s health. Methodology: This work aimed to acquire spectral information from EC images via Spectrum-Aided Visual Enhancer (SAVE) technology, which improves imaging beyond the limitations of conventional White-Light Imaging (WLI). The hyperspectral data acquired using SAVE were examined utilizing sophisticated deep learning methodologies, incorporating models such as YOLOv8, YOLOv7, YOLOv6, YOLOv5, Scaled YOLOv4, and YOLOv3. The models were as

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Panja, Eben, Hendry Hendry, and Christine Dewi. "YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions." Scientific Journal of Informatics 11, no. 1 (2024): 127–38. http://dx.doi.org/10.15294/sji.v11i1.49038.

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Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation

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Khusainov, R. M. "Selection of a Neural Network Model Based on the Hierarchy Process Analysis Method." Vestnik NSU. Series: Information Technologies 22, no. 4 (2025): 62–70. https://doi.org/10.25205/1818-7900-2024-22-4-62-70.

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This article discusses the selection of the optimal neural network model YOLOv8 (YOLOv8s, YOLOv8l, YOLOv8x, YOLOv8m, YOLOv8n) using the hierarchy process analysis method, which allows structuring and systematizing complex decisions based on multi-criteria assessments. The main focus is on identifying and comparative analysis of the most significant criteria for assessing the effectiveness of neural network models, such as training time, as well as the Precision, Recall and F1-score metrics. These metrics play a key role in computer vision tasks, especially when it comes to object detection. Du

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Podder, Soumyajit, Abhishek Mallick, Sudipta Das, Kartik Sau, and Arijit Roy. "Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images." AIMS Biophysics 10, no. 4 (2023): 453–81. http://dx.doi.org/10.3934/biophy.2023026.

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<abstract> <p>Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the

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De Dios Garcia, Julio Cesar, Nantai Nava Nolazco, Ernesto Monroy Cruz, et al. "Comparative study of Convolutional Neural Networks performance and efficiency with YOLOv8 models applied for pest detection purposes in bean plants." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 2 (2025): 112–22. https://doi.org/10.61467/2007.1558.2025.v16i2.603.

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Neural Networks have significantly evolved, particularly in their application to computer vision. This paper presents a comprehensive comparison of different versions of YOLOv8, such as YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x for the detection of pests in bean plants, leveraging the capabilities of Convolutional Neural Networks. To train the neural network using different versions of YOLOv8, identical conditions were applied, such as the amount of environment light, the number of labeled images, epochs, and batch size. The results indicate that, as the complexity of the YOLO model incr

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Liu, Yinzeng, Fandi Zeng, Hongwei Diao, et al. "YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion." Sensors 24, no. 13 (2024): 4379. http://dx.doi.org/10.3390/s24134379.

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Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and glo

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Sun, Daozong, Kai Zhang, Hongsheng Zhong, et al. "Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model." Agriculture 14, no. 3 (2024): 353. http://dx.doi.org/10.3390/agriculture14030353.

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Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model’s ability to extract features. The backbone network’s C2f model is replaced with the VoV-GSCSP module to reduce the mod

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Hanna, Arini Parhusip, Trihandaru Suryasatriya, Indrajaya Denny, and Labadin Jane. "Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3291–305. https://doi.org/10.11591/ijai.v13.i3.pp3291-3305.

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You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number

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Çakmakçı, Cihan. "Dijital Hayvancılıkta Yapay Zekâ ve İnsansız Hava Araçları: Derin Öğrenme ve Bilgisayarlı Görme İle Dağlık ve Engebeli Arazide Kıl Keçisi Tespiti, Takibi ve Sayımı." Turkish Journal of Agriculture - Food Science and Technology 12, no. 7 (2024): 1162–73. http://dx.doi.org/10.24925/turjaf.v12i7.1162-1173.6701.

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Küresel gıda talebindeki hızlı artış nedeniyle yüksek kaliteli hayvansal ürün üretiminin artırılması gerekliliği, modern hayvancılık uygulamalarında teknoloji kullanımı ihtiyacını beraberinde getirmiştir. Özellikle ekstansif koşullarda küçükbaş hayvan yetiştiriciliğinde hayvanların otomatik olarak izlenmesi ve yönetilmesi, verimliliğin artırılması açısından büyük öneme sahiptir. Bu noktada, insansız hava araçlarından elde edilen yüksek çözünürlüklü görüntüler ile derin öğrenme algoritmalarının birleştirilmesi, sürülerin uzaktan takip edilmesinde etkili çözümler sunma potansiyeli taşımaktadır.

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Arini Parhusip, Hanna, Suryasatriya Trihandaru, Denny Indrajaya, and Jane Labadin. "Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3291. http://dx.doi.org/10.11591/ijai.v13.i3.pp3291-3305.

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<p>You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The smal

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Althaf Pramasetya Perkasa, Mochamad, R. Reza El Akbar, Muhammad Al Husaini, and Randi Rizal. "VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE." Jurnal Teknik Informatika (Jutif) 5, no. 3 (2024): 811–20. https://doi.org/10.52436/1.jutif.2024.5.3.2015.

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In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLO

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KHUSAINOV, R. M. "ROAD INFRASTRUCTURE OBJECT RECOGNITION SOFTWARE PACKAGE." Herald of Technological University 28, no. 2 (2025): 61–65. https://doi.org/10.55421/1998-7072_2025_28_2_61.

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The article considers the problem of recognizing road infrastructure objects using the developed mobile application. The necessity of using YOLOv8 neural network models to solve this problem is substantiated. When implementing the mobile application, the Kotlin programming language and the Android Studio 2024.2.1 development environment were chosen. The Driver Assistant mobile application was developed on a laptop with an Intel Pentium CPU 3825U processor with a frequency of 1.9 GHz, 8 GB RAM, running the 64-bit Windows 10 Pro operating system. The main components of the mobile application are

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Salma, Kartika, and Syarif Hidayat. "Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 5, no. 2 (2024): 1611–18. http://dx.doi.org/10.35870/jimik.v5i2.716.

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The implementation of Face Emotion Recognition (FER) technology in online classes opens up new opportunities to effectively monitor students' emotional responses and adjust the teaching approach. Through FER, instructors can monitor students' emotional responses to learning materials in real-time and enable quick adjustments based on individual needs. Additionally, this technology can also be used to detect the level of enthusiasm or lack thereof among students towards the learning process, allowing for the optimization of teaching strategies. This study focuses on the implementation of the YO

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Lou, Haitong, Xuehu Duan, Junmei Guo, et al. "DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor." Electronics 12, no. 10 (2023): 2323. http://dx.doi.org/10.3390/electronics12102323.

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Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object’s category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-

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Taufiqurrahman, Taufiqurrahman, Aji Prasetya Hadi, and Rully Emirza Siregar. "Evaluasi Performa Yolov8 Dalam Deteksi Objek Di Depan Kendaraan Dengan Variasi Kondisi Lingkungan." Jurnal Minfo Polgan 13, no. 2 (2024): 1755–73. http://dx.doi.org/10.33395/jmp.v13i2.14228.

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Keselamatan berlalu lintas adalah isu global yang membutuhkan perhatian serius mengingat tingginya angka kecelakaan setiap tahun. Penelitian ini bertujuan mengevaluasi performa YOLOv8, algoritma deteksi objek berbasis deep learning, dalam mendeteksi elemen-elemen penting lalu lintas seperti kendaraan, pejalan kaki, dan rambu lalu lintas. Dataset yang digunakan terdiri dari video jalan biasa dan jalan tol, direkam pada enam waktu berbeda (08:00, 10:00, 12:00, 18:00, 20:00, dan 22:00) untuk menangkap variasi pencahayaan dan kepadatan lalu lintas. Tiga varian YOLOv8, yaitu YOLOv8n, YOLOv8s, dan Y

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Gong, Chuang, Wei Jiang, Dehua Zou, Weiwei Weng, and Hongjun Li. "An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8." Applied Sciences 14, no. 19 (2024): 8770. http://dx.doi.org/10.3390/app14198770.

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Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature e

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Luo, Qinpeng, and Yinhua Liao. "Detecting Algorithm based on the Improved YOLOv8s for a Weak Feature Defect of Aviation Clamps." International Journal of Mechanical and Electrical Engineering 4, no. 3 (2025): 21–37. https://doi.org/10.62051/ijmee.v4n3.03.

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There are some weak defects on the surface of aviation clamps. Because they are very weak, it is difficult to identify them efficiently and accurately by the existing visual detecting algorithms, and the existing methods have high arithmetic power requirements for vision detecting systems. So, this work proposes a detecting algorithm for a weak feature defect detection of aviation clamps (YOLO-OGS). Firstly, in order to improve the ability of convolutional operations of extract features and decrease the model's GFLOPs, the Multidimensional dynamic convolutional ODConv is added to the backbone

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Kestane, Bahadir Besir, Emin Guney, and Cuneyt Bayilmis. "Real-time Recyclable Waste Detection Using YOLOv8 for Reverse Vending Machines." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 10, no. 2 (2024): 345–58. https://doi.org/10.26555/jiteki.v10i2.29208.

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Increasing challenges in waste management necessitate optimizing the efficiency of recycling systems. Reverse Vending Machines (RVMs) offer a promising solution by incentivizing recycling through user rewards. However, inaccurate waste detection methods hinder the effectiveness of RVMs. This study explores the potential of the YOLOv8 deep learning algorithm to enhance real-time waste classification accuracy in RVMs. We propose a YOLOv8-based framework for real-time detection of seven key recyclable materials. The model is trained on a combined dataset comprising the public TrashNet dataset and

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Richo, Richo. "Analisis Keandalan YOLOv8m untuk Deteksi Varian Produk Kemasan Kotak pada Sistem Manajemen Kesediaan Stock." Informatics and Digital Expert (INDEX) 6, no. 2 (2024): 124–31. https://doi.org/10.36423/index.v6i2.1984.

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Sistem manajemen kesediaan stock secara otomatis dapat meningkatkan efisiensi waktu ketika proses pendataan produk, mempercepat laju penjualan, efisiensi budget, hingga meminimalisir terjadinya tindakan kecurangan pendataan oleh petugas toko. Demi terciptanya tujuan tersebut sudah seharusnya sistem pendataan kesediaan stock dioptimalisasi dengan sistem otomatisasi. Sayangnya, sistem pendataan saat ini masih dilakukan secara manual dengan basis pengecekan produk pada display shelfing sehingga memicu terjadinya kesalahan pendataan oleh petugas, tentunya akar permasalahan ini harus segera diputus

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Asaju, Christine Bukola, Pius Adewale Owolawi, Chuling Tu, and Etienne Van Wyk. "Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection." Information 16, no. 1 (2025): 57. https://doi.org/10.3390/info16010057.

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Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the

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Simanjuntak, Nurchaya, Raymond Erz Saragih, and Yonky Pernando. "Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels." Journal of Computer System and Informatics (JoSYC) 5, no. 3 (2024): 719–28. https://doi.org/10.47065/josyc.v5i3.5123.

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In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the pote

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Gong, He, Jingyi Liu, Zhipeng Li, et al. "GFI-YOLOv8: Sika Deer Posture Recognition Target Detection Method Based on YOLOv8." Animals 14, no. 18 (2024): 2640. http://dx.doi.org/10.3390/ani14182640.

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As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes

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Kutyrev, A. I., I. G. Smirnov, and N. A. Andriyanov. "Neural network models of apple fruit identification in tree crowns: comparative analysis." Horticulture and viticulture, no. 5 (November 30, 2023): 56–63. http://dx.doi.org/10.31676/0235-2591-2023-5-56-63.

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The article presents the results of an analysis conducted from 2022 to 2023 to assess the quality of modern neural network models of apple fruit identification in tree crowns shown in images. In order to conduct the studies on identifying the best detector, the following neural networks were used: SSD (Single Shot MultiBox Detector), YOLOv4 (You Only Look Once, Version 4), YOLOv5, YOLOv7, and YOLOv8. The performance of the considered models of apple fruit identification was assessed using such binary classification metrics as precision, recall, accuracy, F-score, and AUC-ROCTotal (area under t

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Zhang, Yijian, Yong Yin, and Zeyuan Shao. "An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images." Remote Sensing 15, no. 19 (2023): 4818. http://dx.doi.org/10.3390/rs15194818.

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Unmanned aerial vehicles (UAVs), renowned for their rapid deployment, extensive data collection, and high spatial resolution, are crucial in locating distressed individuals during search and rescue (SAR) operations. Challenges in maritime search and rescue include missed detections due to issues including sunlight reflection. In this study, we proposed an enhanced ABT-YOLOv7 algorithm for underwater person detection. This algorithm integrates an asymptotic feature pyramid network (AFPN) to preserve the target feature information. The BiFormer module enhances the model’s perception of small-sca

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Alayed, Asmaa, Rehab Alidrisi, Ekram Feras, Shahad Aboukozzana, and Alaa Alomayri. "Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13290–98. http://dx.doi.org/10.48084/etasr.6753.

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The number of accidental fires in buildings has been significantly increased in recent years in Saudi Arabia. Fire Safety Equipment (FSE) plays a crucial role in reducing fire risks. However, this equipment is prone to defects and requires periodic checks and maintenance. Fire safety inspectors are responsible for visual inspection of safety equipment and reporting defects. As the traditional approach of manually checking each piece of equipment can be time-consuming and inaccurate, this study aims to improve the inspection processes of safety equipment. Using computer vision and deep learning

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Sabillah, Ainul Hakim Fizal, Hafizal Mohamad, Sagaya Sabestinal Amalathas, Hwan Won Seung, and Muhammad Hakim Ahmad Sobri. "ENHANCING OIL PALM FRUIT DETECTION: A COMPARATIVE ANALYSIS OF YOLO ALGORITHMS." International Journal of Innovation and Industrial Revolution 7, no. 20 (2025): 01–18. https://doi.org/10.35631/ijirev.720001.

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The oil palm industry is pivotal in agricultural research because of its importance. The central focus of this study, however, revolves around elevating cutting-edge intelligent techniques in agriculture, specifically for the improved detection of Fresh Fruit Bunches within oil palm plantations. Moreover, Malaysia’s economic impact on oil palm production is explored, emphasizing its position as a leading producer and exporter of palm oil. The study compares and corroborates the performances among a series of YOLO algorithm models, namely YOLOv3, YOLOv4, YOLOv7, and YOLOv8, by exploiting divers

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Sun, Jihong, Zhaowen Li, Fusheng Li, Yingming Shen, Ye Qian, and Tong Li. "EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment." Agronomy 14, no. 9 (2024): 2099. http://dx.doi.org/10.3390/agronomy14092099.

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The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient d

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Jiang, Tao, Jie Zhou, Binbin Xie, et al. "Improved YOLOv8 Model for Lightweight Pigeon Egg Detection." Animals 14, no. 8 (2024): 1226. http://dx.doi.org/10.3390/ani14081226.

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In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Mult

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Ramadhani, Zahra Cahya, and Dimas Firmanda Al Riza. "Model Deteksi Mikroalga Spirulina platensis dan Chlorella vulgaris Berbasis Convolutional Neural Network YOLOv8." Jurnal Komputer dan Informatika 12, no. 2 (2024): 110–19. https://doi.org/10.35508/jicon.v12i2.15375.

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Mikroalga merupakan organisme mikroskopis bersel tunggal yang hidup di berbagai perairan. Mikroalga seperti Spirulina platensis dan Chlorella vulgaris berpotensi menjadi sumber bioenergi sehingga mulai banyak dikultivasi. Kultivasi ini umumnya masih melakukan pemantauan jumlah/kepadatan sel mikroalga secara manual menggunakan hemositometer yang lebih lama dan rentan human error. Penelitian ini bertujuan untuk mengembangkan model deteksi mikroalga Spirulina platensis dan Chlorella vulgaris berbasis citra mikroskopis dan Convolutional Neural Network menggunakan YOLOv8. Metodologi penelitian menc

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V., Mareeswari, Vijayan R., Shajith Nisthar, and Rahul Bala Krishnan. "Traffic Sign Detection and Recognition Using Yolo Models." International Journal of Information Technology and Computer Science 17, no. 3 (2025): 13–25. https://doi.org/10.5815/ijitcs.2025.03.02.

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With the proliferation of advanced driver assistance systems and continued advances in autonomous vehicle technology, there is a need for accurate, real-time methods of identifying and interpreting traffic signs. The importance of traffic sign detection can't be overstated, as it plays a pivotal role in improving road safety and traffic management. This proposed work suggests a unique real-time traffic sign detection and recognition approach using the YOLOv8 algorithm. Utilizing the integrated webcams of personal computers and laptops, we capture live traffic scenes and train our model using a

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Tverdokhlebov, A. S., A. S. Krasnoperova, A. A. Kartashov, V. Y. Kuprits, and V. I. Weber. "Effectiveness of neural network models for automatic detection and recognition of ground objects in infrared images." Ural Radio Engineering Journal 8, no. 4 (2024): 451–68. https://doi.org/10.15826/urej.2024.8.4.003.

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In this paper the study of modern neural network models designed for object recognition in infrared images acquired from unmanned aerial vehicles (UAVs) is carried out. Different YOLO (You Only Look Once) architectures including YOLOv5, YOLOv8 and YOLOv9 versions are considered. Models are evaluated on key metrics such as Precision (Precision), completeness (Recall) and mean accuracy (mAP), taking into account computational requirements. Particular attention is paid to the application of the models under resource constraints and increased data complexity, making them relevant for monitoring an

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Ma, Na, Yulong Wu, Yifan Bo, and Hongwen Yan. "Chili Pepper Object Detection Method Based on Improved YOLOv8n." Plants 13, no. 17 (2024): 2402. http://dx.doi.org/10.3390/plants13172402.

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In response to the low accuracy and slow detection speed of chili recognition in natural environments, this study proposes a chili pepper object detection method based on the improved YOLOv8n. Evaluations were conducted among YOLOv5n, YOLOv6n, YOLOv7-tiny, YOLOv8n, YOLOv9, and YOLOv10 to select the optimal model. YOLOv8n was chosen as the baseline and improved as follows: (1) Replacing the YOLOv8 backbone with the improved HGNetV2 model to reduce floating-point operations and computational load during convolution. (2) Integrating the SEAM (spatially enhanced attention module) into the YOLOv8 d

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Khalid, Saim, Hadi Mohsen Oqaibi, Muhammad Aqib, and Yaser Hafeez. "Small Pests Detection in Field Crops Using Deep Learning Object Detection." Sustainability 15, no. 8 (2023): 6815. http://dx.doi.org/10.3390/su15086815.

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Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early

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Ma, Shihao, Jiao Wu, Zhijun Zhang, and Yala Tong. "Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images." Applied Sciences 14, no. 5 (2024): 2158. http://dx.doi.org/10.3390/app14052158.

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Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster detection. This study evaluated six object detection models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, conducting experiments on remote sensing image data in the study area. Utilizing transfer learning, mudslide remote sensing images were fed into these six models under identical experimental conditions for training. The experimental re

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Puchakayala Lokesh, Lahari, Rahul Gowtham Poola, Leela Prasad Gorrepati, and Siva Sankar Yellampalli. "Real-Time Cataract Diagnosis with GhostYOLO: A GhostConv-enhanced YOLO Model." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 22945–52. https://doi.org/10.48084/etasr.10760.

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This study presents GhostYOLO, an enhanced YOLO-based model for cataract detection that incorporates GhostConv layers to offer greater accuracy, faster processing, and less memory consumption for real-time diagnosis. Initially, the performance of YOLO models, namely YOLOv5, YOLOv6, YOLOv7, and YOLOv8, was evaluated using 788 normal and 920 cataract images, with YOLOv8n emerging as the best standard model with excellent precision, speed, and efficiency. GhostYOLO models were developed to further improve speed and accuracy. GhostYoloV8n obtained the highest accuracy, speed, and lowest memory usa

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Castro-Bello, Mirna, Dominic Brian Roman-Padilla, Cornelio Morales-Morales, et al. "Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management." Sustainability 17, no. 8 (2025): 3523. https://doi.org/10.3390/su17083523.

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Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evalu

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Chen, Haosong, Fujie Zhang, Chaofan Guo, Junjie Yi, and Xiangkai Ma. "SA-SRYOLOv8: A Research on Star Anise Variety Recognition Based on a Lightweight Cascaded Neural Network and Diversified Fusion Dataset." Agronomy 14, no. 10 (2024): 2211. http://dx.doi.org/10.3390/agronomy14102211.

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Star anise, a widely popular spice, benefits from classification that enhances its economic value. In response to the low identification efficiency and accuracy of star anise varieties in the market, as well as the scarcity of related research, this study proposes an efficient identification method based on non-similarity augmentation and a lightweight cascaded neural network. Specifically, this approach utilizes a Siamese enhanced data network and a front-end SRGAN network to address sample imbalance and the challenge of identifying blurred images. The YOLOv8 model is further lightweight to r

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Huang, Nan-Chieh, Arvind Mukundan, Riya Karmakar, Syna Syna, Wen-Yen Chang, and Hsiang-Chen Wang. "Novel Snapshot-Based Hyperspectral Conversion for Dermatological Lesion Detection via YOLO Object Detection Models." Bioengineering 12, no. 7 (2025): 714. https://doi.org/10.3390/bioengineering12070714.

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Objective: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characteristics, potentially delaying diagnosis. Methods: A novel spectrum-aided vision enhancer (SAVE) that transforms standard RGB images into simulated narrowband imaging representations in a single step was proposed. The performances of five cutting-edge object detectors, based on You Look Only Once (YOLOv11, YOLOv10, YOLOv9,

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Mien, Trinh Luong, Nguyen Dinh Tu, and Nguyen Van Lam. "Deploying YOLOv8 for Real-Time Road Crack Detection on Smart Road Length Measurement Devices." Journal of Future Artificial Intelligence and Technologies 2, no. 1 (2025): 135–44. https://doi.org/10.62411/faith.3048-3719-102.

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Nowadays, the construction, monitoring, quality control, and maintenance of roads always require high-precision, easy-to-use measuring devices in the field. This study develops an embedded computer program using the YOLOv8 model integrated into a smart road length measuring device to detect road surface cracks. First, the study analyzes and clarifies the outstanding points of the YOLOv8 model, including anchor-free: Eliminates the use of traditional anchor boxes, helping to simplify the training process, increase accuracy and reduce computational costs; C2f (Cross-Stage Partial with feature fu

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Sahithi, Kothagundla, Kalwakolu Ganesh, ,Mahankali Uday Kiran, and Padma Rajani. "Automatic Detection and Predictive Geo-location of Foreign Object Debris on Airport Runway." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–7. https://doi.org/10.55041/ijsrem.ncft004.

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Abstract—This paper presents an advanced Foreign Object Debris (FOD) detection system for airport runways using YOLOv8 and a geolocation prediction model based on machine learning regression. By integrating a self- attention mechanism with CNNs, the system achieves high detection accuracy, especially for small objects under challenging weather conditions. Ablation studies show improved Mean Average Precision (mAP), and comparisons with YOLOv5, YOLOX, and YOLOv7 confirm YOLOv8's superior performance. The geolocation model further enhances practical deployment for real- world FOD detection and r

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Yang, Shixiong, Jingfa Yao, and Guifa Teng. "Corn Leaf Spot Disease Recognition Based on Improved YOLOv8." Agriculture 14, no. 5 (2024): 666. http://dx.doi.org/10.3390/agriculture14050666.

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Leaf spot disease is an extremely common disease in the growth process of maize in Northern China and its degree of harm is quite significant. Therefore, the rapid and accurate identification of maize leaf spot disease is crucial for reducing economic losses in maize. In complex field environments, traditional identification methods are susceptible to subjective interference and cannot quickly and accurately identify leaf spot disease through color or shape features. We present an advanced disease identification method utilizing YOLOv8. This method utilizes actual field images of diseased corn

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Chen, Aoxiang. "Comparative Analysis of YOLO Variants Based on Performance Evaluation for Object Detection." ITM Web of Conferences 70 (2025): 03008. https://doi.org/10.1051/itmconf/20257003008.

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This study focuses on analysing and exploring the You Only Look Once (YOLO) algorithm. Specifically, this article analyses the evolution and performance of three versions (YOLOv1, YOLOv5, and YOLOv8) in object detection. The research begins by detailing the fundamental concepts of object detection and the datasets commonly used in this field. It then delves into the specific architectures and experimental outcomes associated with each YOLO version. The analysis reveals that while YOLOv8 introduces advanced features and improvements, earlier versions like YOLOv5 may offer superior stability and

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Yang, Renxu, Debao Yuan, Maochen Zhao, et al. "Camellia oleifera Tree Detection and Counting Based on UAV RGB Image and YOLOv8." Agriculture 14, no. 10 (2024): 1789. http://dx.doi.org/10.3390/agriculture14101789.

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The detection and counting of Camellia oleifera trees are important parts of the yield estimation of Camellia oleifera. The ability to identify and count Camellia oleifera trees quickly has always been important in the context of research on the yield estimation of Camellia oleifera. Because of their specific growing environment, it is a difficult task to identify and count Camellia oleifera trees with high efficiency. In this paper, based on a UAV RGB image, three different types of datasets, i.e., a DOM dataset, an original image dataset, and a cropped original image dataset, were designed.

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Xiong, Chenqin, Tarek Zayed, Xingyu Jiang, Ghasan Alfalah, and Eslam Mohammed Abelkader. "A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks—The YOLOv8-AFPN-MPD-IoU." Sensors 24, no. 13 (2024): 4288. http://dx.doi.org/10.3390/s24134288.

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Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures’ performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as

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Hwang, Byeong Hyeon, and Mi Jin Noh. "Comparative Analysis of Toxic Marine Organism Detection Performance Across YOLO Models and Exploration of Applications in Smart Aquaculture Technology." Korean Institute of Smart Media 13, no. 11 (2024): 22–29. https://doi.org/10.30693/smj.2024.13.11.22.

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The rise in sea temperatures due to global warming has accelerated the migration of marine species, leading to the frequent discovery of toxic marine organisms in domestic waters. The blue-ringed octopus in particular is very dangerous because it contains a deadly poison called tetrodotoxin. Therefore, early detection of these toxic species and minimizing the risk to human life is crucial. This study evaluates the effectiveness of using the latest object detection technology, the YOLO model, to detect toxic marine species, aiming to provide valuable information for the development of a smart f

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